input stringlengths 21 146 | metadata stringlengths 226 226 | answers stringlengths 37 1.27k | evidence stringlengths 124 12k |
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what language pairs are explored? | {"label_key": "1912.01214", "label_file": "paper_tab_qa", "q_uid": "5eda469a8a77f028d0c5f1acd296111085614537", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "De-En, En-Fr, Fr-En, En-Es, Ro-En, En-De, Ar-En, En-Ru", "type": "abstractive"}, {"answer": "French-English-Spanish (Fr-En-Es), German-English-French (De-En-Fr) and Romanian-English-German (Ro-En-De), Arabic (Ar), Spanish (Es), and Russian (Ru), and mutual translation between themselves constitutes six zer... | [{"raw_evidence": ["For MultiUN corpus, we use four languages: English (En) is set as the pivot language, which has parallel data with other three languages which do not have parallel data between each other. The three languages are Arabic (Ar), Spanish (Es), and Russian (Ru), and mutual translation between themselves ... |
What accuracy does the proposed system achieve? | {"label_key": "1801.05147", "label_file": "paper_tab_qa", "q_uid": "ef4dba073d24042f24886580ae77add5326f2130", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "F1 scores of 85.99 on the DL-PS data, 75.15 on the EC-MT data and 71.53 on the EC-UQ data ", "type": "abstractive"}, {"answer": "F1 of 85.99 on the DL-PS dataset (dialog domain); 75.15 on EC-MT and 71.53 on EC-UQ (e-commerce domain)", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 2: Main results on the DL-PS data.", "FLOAT SELECTED: Table 3: Main results on the EC-MT and EC-UQ datasets."], "highlighted_evidence": ["FLOAT SELECTED: Table 2: Main results on the DL-PS data.", "FLOAT SELECTED: Table 3: Main results on the EC-MT and EC-UQ datasets."]}, {"raw... |
On which benchmarks they achieve the state of the art? | {"label_key": "1704.06194", "label_file": "paper_tab_qa", "q_uid": "9ee07edc371e014df686ced4fb0c3a7b9ce3d5dc", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "SimpleQuestions, WebQSP", "type": "extractive"}, {"answer": "WebQSP, SimpleQuestions", "type": "extractive"}] | [{"raw_evidence": ["Finally, like STAGG, which uses multiple relation detectors (see yih2015semantic for the three models used), we also try to use the top-3 relation detectors from Section \"Relation Detection Results\" . As shown on the last row of Table 3 , this gives a significant performance boost, resulting in a ... |
How do they calculate a static embedding for each word? | {"label_key": "1909.00512", "label_file": "paper_tab_qa", "q_uid": "891c2001d6baaaf0da4e65b647402acac621a7d2", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "They use the first principal component of a word's contextualized representation in a given layer as its static embedding.", "type": "abstractive"}, {"answer": " by taking the first principal component (PC) of its contextualized representations in a given layer", "type": "extractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: The performance of various static embeddings on word embedding benchmark tasks. The best result for each task is in bold. For the contextualizing models (ELMo, BERT, GPT-2), we use the first principal component of a word’s contextualized representations in a given layer as i... |
What is the performance of BERT on the task? | {"label_key": "2003.03106", "label_file": "paper_tab_qa", "q_uid": "66c96c297c2cffdf5013bab5e95b59101cb38655", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "F1 scores are:\nHUBES-PHI: Detection(0.965), Classification relaxed (0.95), Classification strict (0.937)\nMedoccan: Detection(0.972), Classification (0.967)", "type": "abstractive"}, {"answer": "BERT remains only 0.3 F1-score points behind, and would have achieved the second position among all the MEDDOCA... | [{"raw_evidence": ["To finish with this experiment set, Table also shows the strict classification precision, recall and F1-score for the compared systems. Despite the fact that, in general, the systems obtain high values, BERT outperforms them again. BERT's F1-score is 1.9 points higher than the next most competitive ... |
What state-of-the-art compression techniques were used in the comparison? | {"label_key": "1909.11687", "label_file": "paper_tab_qa", "q_uid": "efe9bad55107a6be7704ed97ecce948a8ca7b1d2", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "baseline without knowledge distillation (termed NoKD), Patient Knowledge Distillation (PKD)", "type": "extractive"}, {"answer": "NoKD, PKD, BERTBASE teacher model", "type": "extractive"}] | [{"raw_evidence": ["For the language modeling evaluation, we also evaluate a baseline without knowledge distillation (termed NoKD), with a model parameterized identically to the distilled student models but trained directly on the teacher model objective from scratch. For downstream tasks, we compare with NoKD as well ... |
What discourse relations does it work best/worst for? | {"label_key": "1804.05918", "label_file": "paper_tab_qa", "q_uid": "f17ca24b135f9fe6bb25dc5084b13e1637ec7744", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "explicit discourse relations", "type": "extractive"}, {"answer": "Best: Expansion (Exp). Worst: Comparison (Comp).", "type": "abstractive"}] | [{"raw_evidence": ["The second row shows the performance of our basic paragraph-level model which predicts both implicit and explicit discourse relations in a paragraph. Compared to the variant system (the first row), the basic model further improved the classification performance on the first three implicit relations.... |
Which 7 Indian languages do they experiment with? | {"label_key": "2002.01664", "label_file": "paper_tab_qa", "q_uid": "75df70ce7aa714ec4c6456d0c51f82a16227f2cb", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Hindi, English, Kannada, Telugu, Assamese, Bengali and Malayalam", "type": "abstractive"}, {"answer": "Kannada, Hindi, Telugu, Malayalam, Bengali, English and Assamese (in table, missing in text)", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Dataset"], "highlighted_evidence": ["FLOAT SELECTED: Table 1: Dataset"]}, {"raw_evidence": ["In this section, we describe our dataset collection process. We collected and curated around 635Hrs of audio data for 7 Indian languages, namely Kannada, Hindi, Telugu, Malayalam, Be... |
Do they use graphical models? | {"label_key": "1809.00540", "label_file": "paper_tab_qa", "q_uid": "a99fdd34422f4231442c220c97eafc26c76508dd", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "No", "type": "boolean"}, {"answer": "No", "type": "boolean"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 2: Clustering results on the labeled dataset. We compare our algorithm (with and without timestamps) with the online micro-clustering routine of Aggarwal and Yu (2006) (denoted by CluStream). The F1 values are for the precision (P) and recall (R) in the following columns. See T... |
What metric is used for evaluation? | {"label_key": "1809.00540", "label_file": "paper_tab_qa", "q_uid": "d604f5fb114169f75f9a38fab18c1e866c5ac28b", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "F1, precision, recall, accuracy", "type": "abstractive"}, {"answer": "Precision, recall, F1, accuracy", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 2: Clustering results on the labeled dataset. We compare our algorithm (with and without timestamps) with the online micro-clustering routine of Aggarwal and Yu (2006) (denoted by CluStream). The F1 values are for the precision (P) and recall (R) in the following columns. See T... |
Which eight NER tasks did they evaluate on? | {"label_key": "2004.03354", "label_file": "paper_tab_qa", "q_uid": "1d3e914d0890fc09311a70de0b20974bf7f0c9fe", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "BC5CDR-disease, NCBI-disease, BC5CDR-chem, BC4CHEMD, BC2GM, JNLPBA, LINNAEUS, Species-800", "type": "abstractive"}, {"answer": "BC5CDR-disease, NCBI-disease, BC5CDR-chem, BC4CHEMD, BC2GM, JNLPBA, LINNAEUS, Species-800", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 2: Top: Examples of within-space and cross-space nearest neighbors (NNs) by cosine similarity in GreenBioBERT’s wordpiece embedding layer. Blue: Original wordpiece space. Green: Aligned Word2Vec space. Bottom: Biomedical NER test set precision / recall / F1 (%) measured with th... |
Do they test their framework performance on commonly used language pairs, such as English-to-German? | {"label_key": "1611.04798", "label_file": "paper_tab_qa", "q_uid": "897ba53ef44f658c128125edd26abf605060fb13", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Yes", "type": "boolean"}, {"answer": "Yes", "type": "boolean"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Results of the English→German systems in a simulated under-resourced scenario."], "highlighted_evidence": ["FLOAT SELECTED: Table 1: Results of the English→German systems in a simulated under-resourced scenario."]}, {"raw_evidence": ["A standard NMT system employs parallel d... |
What languages are evaluated? | {"label_key": "1809.01541", "label_file": "paper_tab_qa", "q_uid": "c32adef59efcb9d1a5b10e1d7c999a825c9e6d9a", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "German, English, Spanish, Finnish, French, Russian, Swedish.", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 2: Official shared task test set results."], "highlighted_evidence": ["FLOAT SELECTED: Table 2: Official shared task test set results."]}] |
What is MSD prediction? | {"label_key": "1809.01541", "label_file": "paper_tab_qa", "q_uid": "32a3c248b928d4066ce00bbb0053534ee62596e7", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "The task of predicting MSD tags: V, PST, V.PCTP, PASS.", "type": "abstractive"}, {"answer": "morphosyntactic descriptions (MSD)", "type": "extractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Example input sentence. Context MSD tags and lemmas, marked in gray, are only available in Track 1. The cyan square marks the main objective of predicting the word form made. The magenta square marks the auxiliary objective of predicting the MSD tag V;PST;V.PTCP;PASS."], "hi... |
What other models do they compare to? | {"label_key": "1809.09194", "label_file": "paper_tab_qa", "q_uid": "d3dbb5c22ef204d85707d2d24284cc77fa816b6c", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "SAN Baseline, BNA, DocQA, R.M-Reader, R.M-Reader+Verifier and DocQA+ELMo", "type": "abstractive"}, {"answer": "BNA, DocQA, R.M-Reader, R.M-Reader + Verifier, DocQA + ELMo, R.M-Reader+Verifier+ELMo", "type": "abstractive"}] | [{"raw_evidence": ["Table TABREF21 reports comparison results in literature published . Our model achieves state-of-the-art on development dataset in setting without pre-trained large language model (ELMo). Comparing with the much complicated model R.M.-Reader + Verifier, which includes several components, our model st... |
How much better than the baseline is LiLi? | {"label_key": "1802.06024", "label_file": "paper_tab_qa", "q_uid": "286078813136943dfafb5155ee15d2429e7601d9", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "In case of Freebase knowledge base, LiLi model had better F1 score than the single model by 0.20 , 0.01, 0.159 for kwn, unk, and all test Rel type. The values for WordNet are 0.25, 0.1, 0.2. \n", "type": "abstractive"}] | [{"raw_evidence": ["Baselines. As none of the existing KBC methods can solve the OKBC problem, we choose various versions of LiLi as baselines.", "Single: Version of LiLi where we train a single prediction model INLINEFORM0 for all test relations.", "Sep: We do not transfer (past learned) weights for initializing INLIN... |
How many labels do the datasets have? | {"label_key": "1809.00530", "label_file": "paper_tab_qa", "q_uid": "6aa2a1e2e3666f2b2a1f282d4cbdd1ca325eb9de", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "719313", "type": "abstractive"}, {"answer": "Book, Electronics, Beauty and Music each have 6000, IMDB 84919, Yelp 231163, Cell Phone 194792 and Baby 160792 labeled data.", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Summary of datasets."], "highlighted_evidence": ["FLOAT SELECTED: Table 1: Summary of datasets."]}, {"raw_evidence": ["Large-scale datasets: We further conduct experiments on four much larger datasets: IMDB (I), Yelp2014 (Y), Cell Phone (C), and Baby (B). IMDB and Yelp2014 w... |
What are the source and target domains? | {"label_key": "1809.00530", "label_file": "paper_tab_qa", "q_uid": "9176d2ba1c638cdec334971c4c7f1bb959495a8e", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Book, electronics, beauty, music, IMDB, Yelp, cell phone, baby, DVDs, kitchen", "type": "abstractive"}, {"answer": "we use set 1 of the source domain as the only source with sentiment label information during training, and we evaluate the trained model on set 1 of the target domain, Book (BK), Electronics ... | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Summary of datasets.", "Most previous works BIBREF0 , BIBREF1 , BIBREF6 , BIBREF7 , BIBREF29 carried out experiments on the Amazon benchmark released by Blitzer et al. ( BIBREF0 ). The dataset contains 4 different domains: Book (B), DVDs (D), Electronics (E), and Kitchen (K)... |
Which datasets are used? | {"label_key": "1912.08960", "label_file": "paper_tab_qa", "q_uid": "b1bc9ae9d40e7065343c12f860a461c7c730a612", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Existential (OneShape, MultiShapes), Spacial (TwoShapes, Multishapes), Quantification (Count, Ratio) datasets are generated from ShapeWorldICE", "type": "abstractive"}, {"answer": "ShapeWorldICE datasets: OneShape, MultiShapes, TwoShapes, MultiShapes, Count, and Ratio", "type": "abstractive"}] | [{"raw_evidence": ["We develop a variety of ShapeWorldICE datasets, with a similar idea to the “skill tasks” in the bAbI framework BIBREF22. Table TABREF4 gives an overview for different ShapeWorldICE datasets we use in this paper. We consider three different types of captioning tasks, each of which focuses on a distin... |
What are previous state of the art results? | {"label_key": "2002.11910", "label_file": "paper_tab_qa", "q_uid": "9da1e124d28b488b0d94998d32aa2fa8a5ebec51", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Overall F1 score:\n- He and Sun (2017) 58.23\n- Peng and Dredze (2017) 58.99\n- Xu et al. (2018) 59.11", "type": "abstractive"}, {"answer": "For Named entity the maximum precision was 66.67%, and the average 62.58%, same values for Recall was 55.97% and 50.33%, and for F1 57.14% and 55.64%. Where for Nomin... | [{"raw_evidence": ["FLOAT SELECTED: Table 1: The results of two previous models, and results of this study, in which we apply a boundary assembling method. Precision, recall, and F1 scores are shown for both named entity and nominal mention. For both tasks and their overall performance, we outperform the other two mode... |
What is the model performance on target language reading comprehension? | {"label_key": "1909.09587", "label_file": "paper_tab_qa", "q_uid": "37be0d479480211291e068d0d3823ad0c13321d3", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Table TABREF6, Table TABREF8", "type": "extractive"}, {"answer": "when testing on English, the F1 score of the model training on Chinese (Zh) is 53.8, F1 score is only 44.1 for the model training on Zh-En", "type": "extractive"}] | [{"raw_evidence": ["Table TABREF6 shows the result of different models trained on either Chinese or English and tested on Chinese. In row (f), multi-BERT is fine-tuned on English but tested on Chinese, which achieves competitive performance compared with QANet trained on Chinese. We also find that multi-BERT trained on... |
What source-target language pairs were used in this work? | {"label_key": "1909.09587", "label_file": "paper_tab_qa", "q_uid": "a3d9b101765048f4b61cbd3eaa2439582ebb5c77", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "En-Fr, En-Zh, En-Jp, En-Kr, Zh-En, Zh-Fr, Zh-Jp, Zh-Kr to English, Chinese or Korean", "type": "abstractive"}, {"answer": "English , Chinese", "type": "extractive"}, {"answer": "English, Chinese, Korean, we translated the English and Chinese datasets into more languages, with Google Translate", "type": "ex... | [{"raw_evidence": ["FLOAT SELECTED: Table 2: EM/F1 score of multi-BERTs fine-tuned on different training sets and tested on different languages (En: English, Fr: French, Zh: Chinese, Jp: Japanese, Kr: Korean, xx-yy: translated from xx to yy). The text in bold means training data language is the same as testing data lan... |
Which baselines did they compare against? | {"label_key": "1809.02286", "label_file": "paper_tab_qa", "q_uid": "0ad4359e3e7e5e5f261c2668fe84c12bc762b3b8", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Various tree structured neural networks including variants of Tree-LSTM, Tree-based CNN, RNTN, and non-tree models including variants of LSTMs, CNNs, residual, and self-attention based networks", "type": "abstractive"}, {"answer": "Sentence classification baselines: RNTN (Socher et al. 2013), AdaMC-RNTN (D... | [{"raw_evidence": ["FLOAT SELECTED: Table 1: The comparison of various models on different sentence classification tasks. We report the test accuracy of each model in percentage. Our SATA Tree-LSTM shows superior or competitive performance on all tasks, compared to previous treestructured models as well as other sophis... |
What baselines did they consider? | {"label_key": "1809.01202", "label_file": "paper_tab_qa", "q_uid": "4cbe5a36b492b99f9f9fea8081fe4ba10a7a0e94", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "state-of-the-art PDTB taggers", "type": "extractive"}, {"answer": "Linear SVM, RBF SVM, and Random Forest", "type": "abstractive"}] | [{"raw_evidence": ["We first use state-of-the-art PDTB taggers for our baseline BIBREF13 , BIBREF12 for the evaluation of the causality prediction of our models ( BIBREF12 requires sentences extracted from the text as its input, so we used our parser to extract sentences from the message). Then, we compare how models w... |
By how much more does PARENT correlate with human judgements in comparison to other text generation metrics? | {"label_key": "1906.01081", "label_file": "paper_tab_qa", "q_uid": "ffa7f91d6406da11ddf415ef094aaf28f3c3872d", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Best proposed metric has average correlation with human judgement of 0.913 and 0.846 compared to best compared metrics result of 0.758 and 0.829 on WikiBio and WebNLG challenge.", "type": "abstractive"}, {"answer": "Their average correlation tops the best other model by 0.155 on WikiBio.", "type": "abstrac... | [{"raw_evidence": ["We use bootstrap sampling (500 iterations) over the 1100 tables for which we collected human annotations to get an idea of how the correlation of each metric varies with the underlying data. In each iteration, we sample with replacement, tables along with their references and all the generated texts... |
Which stock market sector achieved the best performance? | {"label_key": "1812.10479", "label_file": "paper_tab_qa", "q_uid": "b634ff1607ce5756655e61b9a6f18bc736f84c83", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Energy with accuracy of 0.538", "type": "abstractive"}, {"answer": "Energy", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 8: Sector-level performance comparison."], "highlighted_evidence": ["FLOAT SELECTED: Table 8: Sector-level performance comparison."]}, {"raw_evidence": ["FLOAT SELECTED: Table 7: Our volatility model performance compared with GARCH(1,1). Best performance in bold. Our model has ... |
How much does their model outperform existing models? | {"label_key": "1909.08089", "label_file": "paper_tab_qa", "q_uid": "de5b6c25e35b3a6c5e40e350fc5e52c160b33490", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Best proposed model result vs best previous result:\nArxiv dataset: Rouge 1 (43.62 vs 42.81), Rouge L (29.30 vs 31.80), Meteor (21.78 vs 21.35)\nPubmed dataset: Rouge 1 (44.85 vs 44.29), Rouge L (31.48 vs 35.21), Meteor (20.83 vs 20.56)", "type": "abstractive"}, {"answer": "On arXiv dataset, the proposed m... | [{"raw_evidence": ["The performance of all models on arXiv and Pubmed is shown in Table TABREF28 and Table TABREF29 , respectively. Follow the work BIBREF18 , we use the approximate randomization as the statistical significance test method BIBREF32 with a Bonferroni correction for multiple comparisons, at the confidenc... |
What embedding techniques are explored in the paper? | {"label_key": "1609.00559", "label_file": "paper_tab_qa", "q_uid": "8b3d3953454c88bde88181897a7a2c0c8dd87e23", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Skip–gram, CBOW", "type": "extractive"}, {"answer": "integrated vector-res, vector-faith, Skip–gram, CBOW", "type": "extractive"}] | [{"raw_evidence": ["muneeb2015evalutating trained both the Skip–gram and CBOW models over the PubMed Central Open Access (PMC) corpus of approximately 1.25 million articles. They evaluated the models on a subset of the UMNSRS data, removing word pairs that did not occur in their training corpus more than ten times. chi... |
Which other approaches do they compare their model with? | {"label_key": "1904.10503", "label_file": "paper_tab_qa", "q_uid": "5a65ad10ff954d0f27bb3ccd9027e3d8f7f6bb76", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Akbik et al. (2018), Link et al. (2012)", "type": "abstractive"}, {"answer": "They compare to Akbik et al. (2018) and Link et al. (2012).", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 3: Comparison with existing models."], "highlighted_evidence": ["FLOAT SELECTED: Table 3: Comparison with existing models."]}, {"raw_evidence": ["In this paper, we present a deep neural network model for the task of fine-grained named entity classification using ELMo embeddings... |
How is non-standard pronunciation identified? | {"label_key": "1912.01772", "label_file": "paper_tab_qa", "q_uid": "f9bf6bef946012dd42835bf0c547c0de9c1d229f", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Original transcription was labeled with additional labels in [] brackets with nonstandard pronunciation.", "type": "abstractive"}] | [{"raw_evidence": ["In addition, the transcription includes annotations for noises and disfluencies including aborted words, mispronunciations, poor intelligibility, repeated and corrected words, false starts, hesitations, undefined sound or pronunciations, non-verbal articulations, and pauses. Foreign words, in this c... |
What kind of celebrities do they obtain tweets from? | {"label_key": "1909.04002", "label_file": "paper_tab_qa", "q_uid": "4d28c99750095763c81bcd5544491a0ba51d9070", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Amitabh Bachchan, Ariana Grande, Barack Obama, Bill Gates, Donald Trump,\nEllen DeGeneres, J K Rowling, Jimmy Fallon, Justin Bieber, Kevin Durant, Kim Kardashian, Lady Gaga, LeBron James,Narendra Modi, Oprah Winfrey", "type": "abstractive"}, {"answer": "Celebrities from varioius domains - Acting, Music, Po... | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Twitter celebrities in our dataset, with tweet counts before and after filtering (Foll. denotes followers in millions)"], "highlighted_evidence": ["FLOAT SELECTED: Table 1: Twitter celebrities in our dataset, with tweet counts before and after filtering (Foll. denotes follow... |
What summarization algorithms did the authors experiment with? | {"label_key": "1712.00991", "label_file": "paper_tab_qa", "q_uid": "443d2448136364235389039cbead07e80922ec5c", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "LSA, TextRank, LexRank and ILP-based summary.", "type": "abstractive"}, {"answer": "LSA, TextRank, LexRank", "type": "abstractive"}] | [{"raw_evidence": ["We considered a dataset of 100 employees, where for each employee multiple peer comments were recorded. Also, for each employee, a manual summary was generated by an HR personnel. The summaries generated by our ILP-based approach were compared with the corresponding manual summaries using the ROUGE ... |
What evaluation metrics are looked at for classification tasks? | {"label_key": "1712.00991", "label_file": "paper_tab_qa", "q_uid": "fb3d30d59ed49e87f63d3735b876d45c4c6b8939", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Precision, Recall, F-measure, accuracy", "type": "extractive"}, {"answer": "Precision, Recall and F-measure", "type": "extractive"}] | [{"raw_evidence": ["Precision, Recall and F-measure for this multi-label classification are computed using a strategy similar to the one described in BIBREF21 . Let INLINEFORM0 be the set of predicted labels and INLINEFORM1 be the set of actual labels for the INLINEFORM2 instance. Precision and recall for this instance... |
What methods were used for sentence classification? | {"label_key": "1712.00991", "label_file": "paper_tab_qa", "q_uid": "197b276d0610ebfacd57ab46b0b29f3033c96a40", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Logistic Regression, Multinomial Naive Bayes, Random Forest, AdaBoost, Linear SVM, SVM with ADWSK and Pattern-based", "type": "abstractive"}, {"answer": "Logistic Regression, Multinomial Naive Bayes, Random Forest, AdaBoost, Linear SVM, SVM with ADWSK, Pattern-based approach", "type": "abstractive"}] | [{"raw_evidence": ["We randomly selected 2000 sentences from the supervisor assessment corpus and manually tagged them (dataset D1). This labelled dataset contained 705, 103, 822 and 370 sentences having the class labels STRENGTH, WEAKNESS, SUGGESTION or OTHER respectively. We trained several multi-class classifiers on... |
What modern MRC gold standards are analyzed? | {"label_key": "2003.04642", "label_file": "paper_tab_qa", "q_uid": "9ecde59ffab3c57ec54591c3c7826a9188b2b270", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "fit our problem definition and were published in the years 2016 to 2019, have at least $(2019 - publication\\ year) \\times 20$ citations", "type": "extractive"}, {"answer": "MSMARCO, HOTPOTQA, RECORD, MULTIRC, NEWSQA, and DROP.", "type": "abstractive"}] | [{"raw_evidence": ["We select contemporary MRC benchmarks to represent all four commonly used problem definitions BIBREF15. In selecting relevant datasets, we do not consider those that are considered “solved”, i.e. where the state of the art performance surpasses human performance, as is the case with SQuAD BIBREF28, ... |
What was the score of the proposed model? | {"label_key": "1904.07904", "label_file": "paper_tab_qa", "q_uid": "38f58f13c7f23442d5952c8caf126073a477bac0", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Best results authors obtain is EM 51.10 and F1 63.11", "type": "abstractive"}, {"answer": "EM Score of 51.10", "type": "abstractive"}] | [{"raw_evidence": ["To better demonstrate the effectiveness of the proposed model, we compare with baselines and show the results in Table TABREF12 . The baselines are: (a) trained on S-SQuAD, (b) trained on T-SQuAD and then fine-tuned on S-SQuAD, and (c) previous best model trained on S-SQuAD BIBREF5 by using Dr.QA BI... |
What hyperparameters are explored? | {"label_key": "2003.11645", "label_file": "paper_tab_qa", "q_uid": "27275fe9f6a9004639f9ac33c3a5767fea388a98", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Dimension size, window size, architecture, algorithm, epochs, hidden dimension size, learning rate, loss function, optimizer algorithm.", "type": "abstractive"}, {"answer": "Hyperparameters explored were: dimension size, window size, architecture, algorithm and epochs.", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Hyper-parameter choices", "FLOAT SELECTED: Table 2: Network hyper-parameters"], "highlighted_evidence": ["FLOAT SELECTED: Table 1: Hyper-parameter choices", "FLOAT SELECTED: Table 2: Network hyper-parameters"]}, {"raw_evidence": ["To form the vocabulary, words occurring less... |
Do they test both skipgram and c-bow? | {"label_key": "2003.11645", "label_file": "paper_tab_qa", "q_uid": "c2d1387e08cf25cb6b1f482178cca58030e85b70", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Yes", "type": "boolean"}, {"answer": "Yes", "type": "boolean"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Hyper-parameter choices"], "highlighted_evidence": ["FLOAT SELECTED: Table 1: Hyper-parameter choices"]}, {"raw_evidence": ["FLOAT SELECTED: Table 1: Hyper-parameter choices", "To form the vocabulary, words occurring less than 5 times in the corpora were dropped, stop words ... |
what is the state of the art? | {"label_key": "1608.06757", "label_file": "paper_tab_qa", "q_uid": "c2b8ee872b99f698b3d2082d57f9408a91e1b4c1", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Babelfy, DBpedia Spotlight, Entityclassifier.eu, FOX, LingPipe MUC-7, NERD-ML, Stanford NER, TagMe 2", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 2: Comparison of annotators trained for common English news texts (micro-averaged scores on match per annotation span). The table shows micro-precision, recall and NER-style F1 for CoNLL2003, KORE50, ACE2004 and MSNBC datasets."], "highlighted_evidence": ["FLOAT SELECTED: Table... |
Do the authors also analyze transformer-based architectures? | {"label_key": "1806.04330", "label_file": "paper_tab_qa", "q_uid": "8bf7f1f93d0a2816234d36395ab40c481be9a0e0", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "No", "type": "boolean"}, {"answer": "No", "type": "boolean"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Summary of representative neural models for sentence pair modeling. The upper half contains sentence encoding models, and the lower half contains sentence pair interaction models."], "highlighted_evidence": ["FLOAT SELECTED: Table 1: Summary of representative neural models f... |
what were the baselines? | {"label_key": "1904.03288", "label_file": "paper_tab_qa", "q_uid": "2ddb51b03163d309434ee403fef42d6b9aecc458", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "LF-MMI Attention\nSeq2Seq \nRNN-T \nChar E2E LF-MMI \nPhone E2E LF-MMI \nCTC + Gram-CTC", "type": "abstractive"}] | [{"raw_evidence": ["We also evaluate the Jasper model's performance on a conversational English corpus. The Hub5 Year 2000 (Hub5'00) evaluation (LDC2002S09, LDC2005S13) is widely used in academia. It is divided into two subsets: Switchboard (SWB) and Callhome (CHM). The training data for both the acoustic and language ... |
what competitive results did they obtain? | {"label_key": "1904.03288", "label_file": "paper_tab_qa", "q_uid": "e587559f5ab6e42f7d981372ee34aebdc92b646e", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "In case of read speech datasets, their best model got the highest nov93 score of 16.1 and the highest nov92 score of 13.3.\nIn case of Conversational Speech, their best model got the highest SWB of 8.3 and the highest CHM of 19.3. ", "type": "abstractive"}, {"answer": "On WSJ datasets author's best approa... | [{"raw_evidence": ["We trained a smaller Jasper 10x3 model with SGD with momentum optimizer for 400 epochs on a combined WSJ dataset (80 hours): LDC93S6A (WSJ0) and LDC94S13A (WSJ1). The results are provided in Table TABREF29 .", "FLOAT SELECTED: Table 6: WSJ End-to-End Models, WER (%)", "FLOAT SELECTED: Table 7: Hub5’... |
By how much is performance improved with multimodality? | {"label_key": "1909.13714", "label_file": "paper_tab_qa", "q_uid": "f68508adef6f4bcdc0cc0a3ce9afc9a2b6333cc5", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "by 2.3-6.8 points in f1 score for intent recognition and 0.8-3.5 for slot filling", "type": "abstractive"}, {"answer": "F1 score increased from 0.89 to 0.92", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Speech Embeddings Experiments: Precision/Recall/F1-scores (%) of NLU Models"], "highlighted_evidence": ["FLOAT SELECTED: Table 1: Speech Embeddings Experiments: Precision/Recall/F1-scores (%) of NLU Models"]}, {"raw_evidence": ["For incorporating speech embeddings experiment... |
How much is performance improved on NLI? | {"label_key": "1909.03405", "label_file": "paper_tab_qa", "q_uid": "bdc91d1283a82226aeeb7a2f79dbbc57d3e84a1a", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": " improvement on the RTE dataset is significant, i.e., 4% absolute gain over the BERTBase", "type": "extractive"}, {"answer": "The average score improved by 1.4 points over the previous best result.", "type": "abstractive"}] | [{"raw_evidence": ["Table TABREF21 illustrates the experimental results, showing that our method is beneficial for all of NLI tasks. The improvement on the RTE dataset is significant, i.e., 4% absolute gain over the BERTBase. Besides NLI, our model also performs better than BERTBase in the STS task. The STS tasks are s... |
what was the baseline? | {"label_key": "1907.03060", "label_file": "paper_tab_qa", "q_uid": "761de1610e934189850e8fda707dc5239dd58092", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "pivot-based translation relying on a helping language BIBREF10, nduction of phrase tables from monolingual data BIBREF14 , attentional RNN-based model (RNMT) BIBREF2, Transformer model BIBREF18, bi-directional model BIBREF11, multi-to-multi (M2M) model BIBREF8, back-translation BIBREF17", "type": "extracti... | [{"raw_evidence": ["We began with evaluating standard MT paradigms, i.e., PBSMT BIBREF3 and NMT BIBREF1 . As for PBSMT, we also examined two advanced methods: pivot-based translation relying on a helping language BIBREF10 and induction of phrase tables from monolingual data BIBREF14 .", "As for NMT, we compared two typ... |
How larger are the training sets of these versions of ELMo compared to the previous ones? | {"label_key": "1911.10049", "label_file": "paper_tab_qa", "q_uid": "603fee7314fa65261812157ddfc2c544277fcf90", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "By 14 times.", "type": "abstractive"}, {"answer": "up to 1.95 times larger", "type": "abstractive"}] | [{"raw_evidence": ["Recently, ELMoForManyLangs BIBREF6 project released pre-trained ELMo models for a number of different languages BIBREF7. These models, however, were trained on a significantly smaller datasets. They used 20-million-words data randomly sampled from the raw text released by the CoNLL 2017 Shared Task ... |
What is the improvement in performance for Estonian in the NER task? | {"label_key": "1911.10049", "label_file": "paper_tab_qa", "q_uid": "09a1173e971e0fcdbf2fbecb1b077158ab08f497", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "5 percent points.", "type": "abstractive"}, {"answer": "0.05 F1", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 4: The results of NER evaluation task, averaged over 5 training and evaluation runs. The scores are average F1 score of the three named entity classes. The columns show FastText, ELMo, and the difference between them (∆(E − FT ))."], "highlighted_evidence": ["FLOAT SELECTED: Ta... |
what is the state of the art on WSJ? | {"label_key": "1812.06864", "label_file": "paper_tab_qa", "q_uid": "70e9210fe64f8d71334e5107732d764332a81cb1", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "CNN-DNN-BLSTM-HMM", "type": "abstractive"}, {"answer": "HMM-based system", "type": "extractive"}] | [{"raw_evidence": ["Table TABREF11 shows Word Error Rates (WER) on WSJ for the current state-of-the-art and our models. The current best model trained on this dataset is an HMM-based system which uses a combination of convolutional, recurrent and fully connected layers, as well as speaker adaptation, and reaches INLINE... |
what is the size of the augmented dataset? | {"label_key": "1811.12254", "label_file": "paper_tab_qa", "q_uid": "57f23dfc264feb62f45d9a9e24c60bd73d7fe563", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "609", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Speech datasets used. Note that HAPD, HAFP and FP only have samples from healthy subjects. Detailed description in App. 2.", "All datasets shown in Tab. SECREF2 were transcribed manually by trained transcriptionists, employing the same list of annotations and protocols, with... |
How many sentences does the dataset contain? | {"label_key": "1908.05828", "label_file": "paper_tab_qa", "q_uid": "d51dc36fbf6518226b8e45d4c817e07e8f642003", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "3606", "type": "abstractive"}, {"answer": "6946", "type": "extractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Dataset statistics"], "highlighted_evidence": ["FLOAT SELECTED: Table 1: Dataset statistics"]}, {"raw_evidence": ["In order to label our dataset with POS-tags, we first created POS annotated dataset of 6946 sentences and 16225 unique words extracted from POS-tagged Nepali Na... |
What is the baseline? | {"label_key": "1908.05828", "label_file": "paper_tab_qa", "q_uid": "cb77d6a74065cb05318faf57e7ceca05e126a80d", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "CNN modelBIBREF0, Stanford CRF modelBIBREF21", "type": "extractive"}, {"answer": "Bam et al. SVM, Ma and Hovy w/glove, Lample et al. w/fastText, Lample et al. w/word2vec", "type": "abstractive"}] | [{"raw_evidence": ["Similar approaches has been applied to many South Asian languages like HindiBIBREF6, IndonesianBIBREF7, BengaliBIBREF19 and In this paper, we present the neural network architecture for NER task in Nepali language, which doesn't require any manual feature engineering nor any data pre-processing duri... |
What is the size of the dataset? | {"label_key": "1908.05828", "label_file": "paper_tab_qa", "q_uid": "a1b3e2107302c5a993baafbe177684ae88d6f505", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Dataset contains 3606 total sentences and 79087 total entities.", "type": "abstractive"}, {"answer": "ILPRL contains 548 sentences, OurNepali contains 3606 sentences", "type": "abstractive"}] | [{"raw_evidence": ["After much time, we received the dataset from Bal Krishna Bal, ILPRL, KU. This dataset follows standard CoNLL-2003 IOB formatBIBREF25 with POS tags. This dataset is prepared by ILPRL Lab, KU and KEIV Technologies. Few corrections like correcting the NER tags had to be made on the dataset. The statis... |
How many different types of entities exist in the dataset? | {"label_key": "1908.05828", "label_file": "paper_tab_qa", "q_uid": "1462eb312944926469e7cee067dfc7f1267a2a8c", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "OurNepali contains 3 different types of entities, ILPRL contains 4 different types of entities", "type": "abstractive"}, {"answer": "three", "type": "extractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Dataset statistics", "Table TABREF24 presents the total entities (PER, LOC, ORG and MISC) from both of the dataset used in our experiments. The dataset is divided into three parts with 64%, 16% and 20% of the total dataset into training set, development set and test set resp... |
How big is the new Nepali NER dataset? | {"label_key": "1908.05828", "label_file": "paper_tab_qa", "q_uid": "f59f1f5b528a2eec5cfb1e49c87699e0c536cc45", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "3606 sentences", "type": "abstractive"}, {"answer": "Dataset contains 3606 total sentences and 79087 total entities.", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Dataset statistics", "After much time, we received the dataset from Bal Krishna Bal, ILPRL, KU. This dataset follows standard CoNLL-2003 IOB formatBIBREF25 with POS tags. This dataset is prepared by ILPRL Lab, KU and KEIV Technologies. Few corrections like correcting the NER... |
What is the performance improvement of the grapheme-level representation model over the character-level model? | {"label_key": "1908.05828", "label_file": "paper_tab_qa", "q_uid": "9bd080bb2a089410fd7ace82e91711136116af6c", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "On OurNepali test dataset Grapheme-level representation model achieves average 0.16% improvement, on ILPRL test dataset it achieves maximum 1.62% improvement", "type": "abstractive"}, {"answer": "BiLSTM+CNN(grapheme-level) which turns out to be performing on par with BiLSTM+CNN(character-level) under the s... | [{"raw_evidence": ["FLOAT SELECTED: Table 5: Comparison of different variation of our models"], "highlighted_evidence": ["FLOAT SELECTED: Table 5: Comparison of different variation of our models"]}, {"raw_evidence": ["We also present a neural architecture BiLSTM+CNN(grapheme-level) which turns out to be performing on p... |
What is the performance of classifiers? | {"label_key": "2002.02070", "label_file": "paper_tab_qa", "q_uid": "d53299fac8c94bd0179968eb868506124af407d1", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Table TABREF10, The KNN classifier seem to perform the best across all four metrics. This is probably due to the multi-class nature of the data set, While these classifiers did not perform particularly well, they provide a good starting point for future work on this subject", "type": "extractive"}, {"ans... | [{"raw_evidence": ["In order to evaluate our classifiers, we perform 4-fold cross validation on a shuffled data set. Table TABREF10 shows the F1 micro and F1 macro scores for all the classifiers. The KNN classifier seem to perform the best across all four metrics. This is probably due to the multi-class nature of the d... |
What classifiers have been trained? | {"label_key": "2002.02070", "label_file": "paper_tab_qa", "q_uid": "29f2954098f055fb19d9502572f085862d75bf61", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "KNN\nRF\nSVM\nMLP", "type": "abstractive"}, {"answer": " K Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), Multi-layer Perceptron (MLP)", "type": "extractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 2: Evaluation metrics for all classifiers.", "In order to evaluate our classifiers, we perform 4-fold cross validation on a shuffled data set. Table TABREF10 shows the F1 micro and F1 macro scores for all the classifiers. The KNN classifier seem to perform the best across all f... |
What other sentence embeddings methods are evaluated? | {"label_key": "1908.10084", "label_file": "paper_tab_qa", "q_uid": "e2db361ae9ad9dbaa9a85736c5593eb3a471983d", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "GloVe, BERT, Universal Sentence Encoder, TF-IDF, InferSent", "type": "abstractive"}, {"answer": "Avg. GloVe embeddings, Avg. fast-text embeddings, Avg. BERT embeddings, BERT CLS-vector, InferSent - GloVe and Universal Sentence Encoder.", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Spearman rank correlation ρ between the cosine similarity of sentence representations and the gold labels for various Textual Similarity (STS) tasks. Performance is reported by convention as ρ × 100. STS12-STS16: SemEval 2012-2016, STSb: STSbenchmark, SICK-R: SICK relatednes... |
which non-english language had the best performance? | {"label_key": "1806.04511", "label_file": "paper_tab_qa", "q_uid": "e79a5b6b6680bd2f63e9f4adbaae1d7795d81e38", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Russian", "type": "extractive"}, {"answer": "Russsian", "type": "abstractive"}] | [{"raw_evidence": ["Considering the improvements over the majority baseline achieved by the RNN model for both non-English (on the average 22.76% relative improvement; 15.82% relative improvement on Spanish, 72.71% vs. 84.21%, 30.53% relative improvement on Turkish, 56.97% vs. 74.36%, 37.13% relative improvement on Dut... |
How big is the dataset used in this work? | {"label_key": "1910.06592", "label_file": "paper_tab_qa", "q_uid": "3e1829e96c968cbd8ad8e9ce850e3a92a76b26e4", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Total dataset size: 171 account (522967 tweets)", "type": "abstractive"}, {"answer": "212 accounts", "type": "abstractive"}] | [{"raw_evidence": ["Data. We build a dataset of Twitter accounts based on two lists annotated in previous works. For the non-factual accounts, we rely on a list of 180 Twitter accounts from BIBREF1. This list was created based on public resources where suspicious Twitter accounts were annotated with the main fake news ... |
What is the size of the new dataset? | {"label_key": "1902.09666", "label_file": "paper_tab_qa", "q_uid": "74fb77a624ea9f1821f58935a52cca3086bb0981", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "14,100 tweets", "type": "abstractive"}, {"answer": "Dataset contains total of 14100 annotations.", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 3: Distribution of label combinations in OLID."], "highlighted_evidence": ["FLOAT SELECTED: Table 3: Distribution of label combinations in OLID."]}, {"raw_evidence": ["FLOAT SELECTED: Table 3: Distribution of label combinations in OLID.", "The data included in OLID has been col... |
How long is the dataset for each step of hierarchy? | {"label_key": "1902.09666", "label_file": "paper_tab_qa", "q_uid": "1b72aa2ec3ce02131e60626639f0cf2056ec23ca", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Level A: 14100 Tweets\nLevel B: 4640 Tweets\nLevel C: 4089 Tweets", "type": "abstractive"}] | [{"raw_evidence": ["The data included in OLID has been collected from Twitter. We retrieved the data using the Twitter API by searching for keywords and constructions that are often included in offensive messages, such as `she is' or `to:BreitBartNews'. We carried out a first round of trial annotation of 300 instances ... |
What different correlations result when using different variants of ROUGE scores? | {"label_key": "1604.00400", "label_file": "paper_tab_qa", "q_uid": "bf52c01bf82612d0c7bbf2e6a5bb2570c322936f", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "we observe that many variants of Rouge scores do not have high correlations with human pyramid scores", "type": "extractive"}, {"answer": "Using Pearson corelation measure, for example, ROUGE-1-P is 0.257 and ROUGE-3-F 0.878.", "type": "abstractive"}] | [{"raw_evidence": ["Table TABREF23 shows the Pearson, Spearman and Kendall correlation of Rouge and Sera, with pyramid scores. Both Rouge and Sera are calculated with stopwords removed and with stemming. Our experiments with inclusion of stopwords and without stemming showed similar results and thus, we do not include ... |
What tasks were evaluated? | {"label_key": "1810.12196", "label_file": "paper_tab_qa", "q_uid": "52f8a3e3cd5d42126b5307adc740b71510a6bdf5", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "ReviewQA's test set", "type": "extractive"}, {"answer": "Detection of an aspect in a review, Prediction of the customer general satisfaction, Prediction of the global trend of an aspect in a given review, Prediction of whether the rating of a given aspect is above or under a given value, Prediction of the ... | [{"raw_evidence": ["Table TABREF19 displays the performance of the 4 baselines on the ReviewQA's test set. These results are the performance achieved by our own implementation of these 4 models. According to our results, the simple LSTM network and the MemN2N perform very poorly on this dataset. Especially on the most ... |
What are their results on both datasets? | {"label_key": "1707.05236", "label_file": "paper_tab_qa", "q_uid": "ab9b0bde6113ffef8eb1c39919d21e5913a05081", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Combining pattern based and Machine translation approaches gave the best overall F0.5 scores. It was 49.11 for FCE dataset , 21.87 for the first annotation of CoNLL-14, and 30.13 for the second annotation of CoNLL-14. ", "type": "abstractive"}] | [{"raw_evidence": ["The error detection results can be seen in Table TABREF4 . We use INLINEFORM0 as the main evaluation measure, which was established as the preferred measure for error correction and detection by the CoNLL-14 shared task BIBREF3 . INLINEFORM1 calculates a weighted harmonic mean of precision and recal... |
Does this method help in sentiment classification task improvement? | {"label_key": "1908.11047", "label_file": "paper_tab_qa", "q_uid": "f2155dc4aeab86bf31a838c8ff388c85440fce6e", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Yes", "type": "boolean"}, {"answer": "No", "type": "boolean"}] | [{"raw_evidence": ["Results are shown in Table TABREF12. Consistent with previous findings, cwrs offer large improvements across all tasks. Though helpful to span-level task models without cwrs, shallow syntactic features offer little to no benefit to ELMo models. mSynC's performance is similar. This holds even for phr... |
For how many probe tasks the shallow-syntax-aware contextual embedding perform better than ELMo’s embedding? | {"label_key": "1908.11047", "label_file": "paper_tab_qa", "q_uid": "ed6a15f0f7fa4594e51d5bde21cc0c6c1bedbfdc", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks", "type": "extractive"}, {"answer": "3", "type": "abstractive"}] | [{"raw_evidence": ["Results in Table TABREF13 show ten probes. Again, we see the performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks. As we would expect, on the probe for predicting chunk tags, mSynC achieves 96.9 $F_1$ vs. 92.2 $F_1$ for ELMo-transformer,... |
What are the black-box probes used? | {"label_key": "1908.11047", "label_file": "paper_tab_qa", "q_uid": "4d706ce5bde82caf40241f5b78338ea5ee5eb01e", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "CCG Supertagging CCGBank , PTB part-of-speech tagging, EWT part-of-speech tagging,\nChunking, Named Entity Recognition, Semantic Tagging, Grammar Error Detection, Preposition Supersense Role, Preposition Supersense Function, Event Factuality Detection", "type": "abstractive"}, {"answer": "Probes are linear... | [{"raw_evidence": ["Recent work has probed the knowledge encoded in cwrs and found they capture a surprisingly large amount of syntax BIBREF10, BIBREF1, BIBREF11. We further examine the contextual embeddings obtained from the enhanced architecture and a shallow syntactic context, using black-box probes from BIBREF1. Ou... |
What are improvements for these two approaches relative to ELMo-only baselines? | {"label_key": "1908.11047", "label_file": "paper_tab_qa", "q_uid": "86bf75245358f17e35fc133e46a92439ac86d472", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "only modest gains on three of the four downstream tasks", "type": "extractive"}, {"answer": " the performance differences across all tasks are small enough ", "type": "extractive"}] | [{"raw_evidence": ["Results in Table TABREF13 show ten probes. Again, we see the performance of baseline ELMo-transformer and mSynC are similar, with mSynC doing slightly worse on 7 out of 9 tasks. As we would expect, on the probe for predicting chunk tags, mSynC achieves 96.9 $F_1$ vs. 92.2 $F_1$ for ELMo-transformer,... |
What are the industry classes defined in this paper? | {"label_key": "1612.08205", "label_file": "paper_tab_qa", "q_uid": "cd2878c5a52542ddf080b20bec005d9a74f2d916", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "technology, religion, fashion, publishing, sports or recreation, real estate, agriculture/environment, law, security/military, tourism, construction, museums or libraries, banking/investment banking, automotive", "type": "abstractive"}, {"answer": "Technology, Religion, Fashion, Publishing, Sports coach, R... | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Industry categories and number of users per category."], "highlighted_evidence": ["FLOAT SELECTED: Table 1: Industry categories and number of users per category."]}, {"raw_evidence": ["FLOAT SELECTED: Table 7: Three top-ranked words for each industry."], "highlighted_evidenc... |
Do they report results only on English data? | {"label_key": "1907.09369", "label_file": "paper_tab_qa", "q_uid": "fd2c6c26fd0ab3c10aae4f2550c5391576a77491", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Yes", "type": "boolean"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 2: Results of final classification in Wang et al."], "highlighted_evidence": ["FLOAT SELECTED: Table 2: Results of final classification in Wang et al."]}] |
Does the paper report the performance of a baseline model on South African languages LID? | {"label_key": "1911.07555", "label_file": "paper_tab_qa", "q_uid": "307e8ab37b67202fe22aedd9a98d9d06aaa169c5", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Yes", "type": "boolean"}, {"answer": "Yes", "type": "boolean"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 2: LID Accuracy Results. The models we executed ourselves are marked with *. The results that are not available from our own tests or the literature are indicated with ’—’."], "highlighted_evidence": ["FLOAT SELECTED: Table 2: LID Accuracy Results. The models we executed oursel... |
Does the algorithm improve on the state-of-the-art methods? | {"label_key": "1911.07555", "label_file": "paper_tab_qa", "q_uid": "e5c8e9e54e77960c8c26e8e238168a603fcdfcc6", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Yes", "type": "boolean"}, {"answer": "From all reported results proposed method (NB+Lex) shows best accuracy on all 3 datasets - some models are not evaluated and not available in literature.", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 2: LID Accuracy Results. The models we executed ourselves are marked with *. The results that are not available from our own tests or the literature are indicated with ’—’."], "highlighted_evidence": ["FLOAT SELECTED: Table 2: LID Accuracy Results. The models we executed oursel... |
Is the dataset balanced between speakers of different L1s? | {"label_key": "1804.11346", "label_file": "paper_tab_qa", "q_uid": "2ceced87af4c8fdebf2dc959aa700a5c95bd518f", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "No", "type": "boolean"}, {"answer": "No", "type": "boolean"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 2: Distribution by L1s and source corpora."], "highlighted_evidence": ["FLOAT SELECTED: Table 2: Distribution by L1s and source corpora."]}, {"raw_evidence": ["FLOAT SELECTED: Table 2: Distribution by L1s and source corpora."], "highlighted_evidence": ["FLOAT SELECTED: Table 2:... |
What state-of-the-art results are achieved? | {"label_key": "1909.00175", "label_file": "paper_tab_qa", "q_uid": "badc9db40adbbf2ea7bac29f2e4e3b6b9175b1f9", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "F1 score of 92.19 on homographic pun detection, 80.19 on homographic pun location, 89.76 on heterographic pun detection.", "type": "abstractive"}, {"answer": "for the homographic dataset F1 score of 92.19 and 80.19 on detection and location and for the heterographic dataset F1 score of 89.76 on detection",... | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Comparison results on two benchmark datasets. (P.: Precision, R.: Recall, F1: F1 score.)"], "highlighted_evidence": ["FLOAT SELECTED: Table 1: Comparison results on two benchmark datasets. (P.: Precision, R.: Recall, F1: F1 score.)"]}, {"raw_evidence": ["FLOAT SELECTED: Tabl... |
What baselines do they compare with? | {"label_key": "1909.00175", "label_file": "paper_tab_qa", "q_uid": "67b66fe67a3cb2ce043070513664203e564bdcbd", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "They compare with the following models: by Pedersen (2017), by Pramanick and Das (2017), by Mikhalkova and Karyakin (2017), by Vadehra (2017), Indurthi and Oota (2017), by Vechtomova (2017), by (Cai et al., 2018), and CRF.", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Comparison results on two benchmark datasets. (P.: Precision, R.: Recall, F1: F1 score.)"], "highlighted_evidence": ["FLOAT SELECTED: Table 1: Comparison results on two benchmark datasets. (P.: Precision, R.: Recall, F1: F1 score.)"]}] |
How big are significant improvements? | {"label_key": "1910.06036", "label_file": "paper_tab_qa", "q_uid": "92294820ac0d9421f086139e816354970f066d8a", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Metrics show better results on all metrics compared to baseline except Bleu1 on Zhou split (worse by 0.11 compared to baseline). Bleu1 score on DuSplit is 45.66 compared to best baseline 43.47, other metrics on average by 1", "type": "abstractive"}] | [{"raw_evidence": ["Table TABREF30 shows automatic evaluation results for our model and baselines (copied from their papers). Our proposed model which combines structured answer-relevant relations and unstructured sentences achieves significant improvements over proximity-based answer-aware models BIBREF9, BIBREF15 on ... |
What was their highest MRR score? | {"label_key": "2002.01984", "label_file": "paper_tab_qa", "q_uid": "9ec1f88ceec84a10dc070ba70e90a792fba8ce71", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "0.5115", "type": "abstractive"}, {"answer": "0.6103", "type": "extractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Factoid Questions. In Batch 3 we obtained the highest score. Also the relative distance between our best system and the top performing system shrunk between Batch 4 and 5."], "highlighted_evidence": ["FLOAT SELECTED: Table 1: Factoid Questions. In Batch 3 we obtained the hig... |
Do the authors hypothesize that humans' robustness to noise is due to their general knowledge? | {"label_key": "1809.03449", "label_file": "paper_tab_qa", "q_uid": "52f9cd05d8312ae3c7a43689804bac63f7cac34b", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Yes", "type": "boolean"}, {"answer": "Yes", "type": "boolean"}] | [{"raw_evidence": ["To verify the effectiveness of general knowledge, we first study the relationship between the amount of general knowledge and the performance of KAR. As shown in Table TABREF13 , by increasing INLINEFORM0 from 0 to 5 in the data enrichment method, the amount of general knowledge rises monotonically,... |
What is the previous state-of-the-art in summarization? | {"label_key": "1903.09722", "label_file": "paper_tab_qa", "q_uid": "ab0fd94dfc291cf3e54e9b7a7f78b852ddc1a797", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "BIBREF26 ", "type": "extractive"}, {"answer": "BIBREF26", "type": "extractive"}] | [{"raw_evidence": ["Following BIBREF11 , we experiment on the non-anonymized version of . When generating summaries, we follow standard practice of tuning the maximum output length and disallow repeating the same trigram BIBREF27 , BIBREF14 . For this task we train language model representations on the combination of n... |
Does the method achieve sota performance on this dataset? | {"label_key": "1806.11432", "label_file": "paper_tab_qa", "q_uid": "701571680724c05ca70c11bc267fb1160ea1460a", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "No", "type": "boolean"}] | [{"raw_evidence": ["That said, these results, though they do show a marginal increase in dev accuracy and a decrease in CE loss, suggest that perhaps listing description is not too predictive of occupancy rate given our parameterizations. While the listing description is surely an influential metric in determining the ... |
What are the baselines used in the paper? | {"label_key": "1806.11432", "label_file": "paper_tab_qa", "q_uid": "600b097475b30480407ce1de81c28c54a0b3b2f8", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "GloVe vectors trained on Wikipedia Corpus with ensembling, and GloVe vectors trained on Airbnb Data without ensembling", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Results of RNN/LSTM"], "highlighted_evidence": ["FLOAT SELECTED: Table 1: Results of RNN/LSTM"]}] |
How better is performance compared to previous state-of-the-art models? | {"label_key": "1910.14537", "label_file": "paper_tab_qa", "q_uid": "5fda8539a97828e188ba26aad5cda1b9dd642bc8", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "F1 score of 97.5 on MSR and 95.7 on AS", "type": "abstractive"}, {"answer": "MSR: 97.7 compared to 97.5 of baseline\nAS: 95.7 compared to 95.6 of baseline", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 5: Results on PKU and MSR compared with previous models in closed test. The asterisks indicate the result of model with unsupervised label from (Wang et al., 2019).", "FLOAT SELECTED: Table 6: Results on AS and CITYU compared with previous models in closed test. The asterisks i... |
What are strong baselines model is compared to? | {"label_key": "1910.14537", "label_file": "paper_tab_qa", "q_uid": "fabcd71644bb63559d34b38d78f6ef87c256d475", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Baseline models are:\n- Chen et al., 2015a\n- Chen et al., 2015b\n- Liu et al., 2016\n- Cai and Zhao, 2016\n- Cai et al., 2017\n- Zhou et al., 2017\n- Ma et al., 2018\n- Wang et al., 2019", "type": "abstractive"}] | [{"raw_evidence": ["Tables TABREF25 and TABREF26 reports the performance of recent models and ours in terms of closed test setting. Without the assistance of unsupervised segmentation features userd in BIBREF20, our model outperforms all the other models in MSR and AS except BIBREF18 and get comparable performance in P... |
which neural embedding model works better? | {"label_key": "1702.03342", "label_file": "paper_tab_qa", "q_uid": "2a6003a74d051d0ebbe62e8883533a5f5e55078b", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "the CRX model", "type": "abstractive"}, {"answer": "3C model", "type": "extractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 5 Accuracy of concept categorization"], "highlighted_evidence": ["FLOAT SELECTED: Table 5 Accuracy of concept categorization"]}, {"raw_evidence": ["Table 3 presents the results of fine-grained dataless classification measured in micro-averaged F1. As we can notice, ESA achieves... |
What is the degree of dimension reduction of the efficient aggregation method? | {"label_key": "1702.03342", "label_file": "paper_tab_qa", "q_uid": "1b1b0c71f1a4b37c6562d444f75c92eb2c727d9b", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "The number of dimensions can be reduced by up to 212 times.", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 8 Evaluation results of dataless document classification of coarse-grained classes measured in micro-averaged F1 along with # of dimensions (concepts) at which corresponding performance is achieved"], "highlighted_evidence": ["FLOAT SELECTED: Table 8 Evaluation results of datal... |
For which languages do they build word embeddings for? | {"label_key": "1805.03710", "label_file": "paper_tab_qa", "q_uid": "9c44df7503720709eac933a15569e5761b378046", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "English", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 2: We generate vectors for OOV using subword information and search for the nearest (cosine distance) words in the embedding space. The LV-M segmentation for each word is: {〈hell, o, o, o〉}, {〈marvel, i, cious〉}, {〈louis, ana〉}, {〈re, re, read〉}, {〈 tu, z, read〉}. We omit the L... |
How big was the corpora they trained ELMo on? | {"label_key": "1909.03135", "label_file": "paper_tab_qa", "q_uid": "d509081673f5667060400eb325a8050fa5db7cc8", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "2174000000, 989000000", "type": "abstractive"}, {"answer": "2174 million tokens for English and 989 million tokens for Russian", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Training corpora", "For the experiments described below, we trained our own ELMo models from scratch. For English, the training corpus consisted of the English Wikipedia dump from February 2017. For Russian, it was a concatenation of the Russian Wikipedia dump from December ... |
What dataset is used? | {"label_key": "1804.07789", "label_file": "paper_tab_qa", "q_uid": "6cd25c637c6b772ce29e8ee81571e8694549c5ab", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "English WIKIBIO, French WIKIBIO , German WIKIBIO ", "type": "abstractive"}, {"answer": "WikiBio dataset, introduce two new biography datasets, one in French and one in German", "type": "extractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Comparison of different models on the English WIKIBIO dataset", "FLOAT SELECTED: Table 4: Comparison of different models on the French WIKIBIO dataset", "FLOAT SELECTED: Table 5: Comparison of different models on the German WIKIBIO dataset"], "highlighted_evidence": ["FLOAT ... |
what topics did they label? | {"label_key": "1810.12085", "label_file": "paper_tab_qa", "q_uid": "ceb767e33fde4b927e730f893db5ece947ffb0d8", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Demographics Age, DiagnosisHistory, MedicationHistory, ProcedureHistory, Symptoms/Signs, Vitals/Labs, Procedures/Results, Meds/Treatments, Movement, Other.", "type": "abstractive"}, {"answer": "Demographics, Diagnosis History, Medication History, Procedure History, Symptoms, Labs, Procedures, Treatments, H... | [{"raw_evidence": ["FLOAT SELECTED: Table 1. HPI Categories and Annotation Instructions"], "highlighted_evidence": ["FLOAT SELECTED: Table 1. HPI Categories and Annotation Instructions"]}, {"raw_evidence": ["We developed a classifier to label topics in the history of present illness (HPI) notes, including demographics,... |
did they compare with other extractive summarization methods? | {"label_key": "1810.12085", "label_file": "paper_tab_qa", "q_uid": "c2cb6c4500d9e02fc9a1bdffd22c3df69655189f", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "No", "type": "boolean"}] | [{"raw_evidence": ["We evaluated our model on the 515 annotated history of present illness notes, which were split in a 70% train set, 15% development set, and a 15% test set. The model is trained using the Adam algorithm for gradient-based optimization BIBREF25 with an initial learning rate = 0.001 and decay = 0.9. A ... |
what levels of document preprocessing are looked at? | {"label_key": "1610.07809", "label_file": "paper_tab_qa", "q_uid": "06eb9f2320451df83e27362c22eb02f4a426a018", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "raw text, text cleaning through document logical structure detection, removal of keyphrase sparse sections of the document", "type": "extractive"}, {"answer": "Level 1, Level 2 and Level 3.", "type": "abstractive"}] | [{"raw_evidence": ["While previous work clearly states that efficient document preprocessing is a prerequisite for the extraction of high quality keyphrases, there is, to our best knowledge, no empirical evidence of how preprocessing affects keyphrase extraction performance. In this paper, we re-assess the performance ... |
How many different phenotypes are present in the dataset? | {"label_key": "2003.03044", "label_file": "paper_tab_qa", "q_uid": "46c9e5f335b2927db995a55a18b7c7621fd3d051", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "15 clinical patient phenotypes", "type": "extractive"}, {"answer": "Thirteen different phenotypes are present in the dataset.", "type": "abstractive"}] | [{"raw_evidence": ["We have created a dataset of discharge summaries and nursing notes, all in the English language, with a focus on frequently readmitted patients, labeled with 15 clinical patient phenotypes believed to be associated with risk of recurrent Intensive Care Unit (ICU) readmission per our domain experts (... |
What are 10 other phenotypes that are annotated? | {"label_key": "2003.03044", "label_file": "paper_tab_qa", "q_uid": "ce0e2a8675055a5468c4c54dbb099cfd743df8a7", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Adv. Heart Disease, Adv. Lung Disease, Alcohol Abuse, Chronic Neurologic Dystrophies, Dementia, Depression, Developmental Delay, Obesity, Psychiatric disorders and Substance Abuse", "type": "abstractive"}] | [{"raw_evidence": ["Table defines each of the considered clinical patient phenotypes. Table counts the occurrences of these phenotypes across patient notes and Figure contains the corresponding correlation matrix. Lastly, Table presents an overview of some descriptive statistics on the patient notes' lengths.", "FLOAT ... |
HOw does the method perform compared with baselines? | {"label_key": "1909.00015", "label_file": "paper_tab_qa", "q_uid": "f8c1b17d265a61502347c9a937269b38fc3fcab1", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "On the datasets DE-EN, JA-EN, RO-EN, and EN-DE, the baseline achieves 29.79, 21.57, 32.70, and 26.02 BLEU score, respectively. The 1.5-entmax achieves 29.83, 22.13, 33.10, and 25.89 BLEU score, which is a difference of +0.04, +0.56, +0.40, and -0.13 BLEU score versus the baseline. The α-entmax achieves 2... | [{"raw_evidence": ["FLOAT SELECTED: Table 1: Machine translation tokenized BLEU test results on IWSLT 2017 DE EN, KFTT JA EN, WMT 2016 RO EN and WMT 2014 EN DE, respectively.", "We report test set tokenized BLEU BIBREF32 results in Table TABREF27. We can see that replacing softmax by entmax does not hurt performance in... |
What evaluation metrics did look at? | {"label_key": "1705.01214", "label_file": "paper_tab_qa", "q_uid": "cc608df2884e1e82679f663ed9d9d67a4b6c03f3", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "precision, recall, F1 and accuracy", "type": "abstractive"}, {"answer": "Response time, resource consumption (memory, CPU, network bandwidth), precision, recall, F1, accuracy.", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 15: Evaluation of different classifiers in the first version of the training set"], "highlighted_evidence": ["FLOAT SELECTED: Table 15: Evaluation of different classifiers in the first version of the training set"]}, {"raw_evidence": ["In this section, we describe the validatio... |
How much improvement is gained from Adversarial Reward Augmented Maximum Likelihood (ARAML)? | {"label_key": "1908.07195", "label_file": "paper_tab_qa", "q_uid": "79f9468e011670993fd162543d1a4b3dd811ac5d", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "ARAM has achieved improvement over all baseline methods using reverese perplexity and slef-BLEU metric. The maximum reverse perplexity improvement 936,16 is gained for EMNLP2017 WMT dataset and 48,44 for COCO dataset.", "type": "abstractive"}, {"answer": "Compared to the baselines, ARAML does not do b... | [{"raw_evidence": ["FLOAT SELECTED: Table 4: Automatic evaluation on COCO and EMNLP2017 WMT. Each metric is presented with mean and standard deviation."], "highlighted_evidence": ["FLOAT SELECTED: Table 4: Automatic evaluation on COCO and EMNLP2017 WMT. Each metric is presented with mean and standard deviation."]}, {"r... |
what was their character error rate? | {"label_key": "1703.07090", "label_file": "paper_tab_qa", "q_uid": "1bb7eb5c3d029d95d1abf9f2892c1ec7b6eef306", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "2.49% for layer-wise training, 2.63% for distillation, 6.26% for transfer learning.", "type": "abstractive"}, {"answer": "Their best model achieved a 2.49% Character Error Rate.", "type": "abstractive"}] | [{"raw_evidence": ["FLOAT SELECTED: Table 3. The CER and RTF of 9-layers, 2-layers regular-trained and 2-laryers distilled LSTM.", "FLOAT SELECTED: Table 2. The CER of 6 to 9-layers models trained by regular Xavier Initialization, layer-wise training with CE criterion and CE + sMBR criteria. The teacher of 9-layer mode... |
which lstm models did they compare with? | {"label_key": "1703.07090", "label_file": "paper_tab_qa", "q_uid": "c0af8b7bf52dc15e0b33704822c4a34077e09cd1", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "Unidirectional LSTM networks with 2, 6, 7, 8, and 9 layers.", "type": "abstractive"}] | [{"raw_evidence": ["There is a high real time requirement in real world application, especially in online voice search system. Shenma voice search is one of the most popular mobile search engines in China, and it is a streaming service that intermediate recognition results displayed while users are still speaking. Unid... |
What was the baseline? | {"label_key": "1707.03569", "label_file": "paper_tab_qa", "q_uid": "37edc25e39515ffc2d92115d2fcd9e6ceb18898b", "benchmark_name": "uda_paper_tab_qa", "benchmark_type": "uda", "sub_benchmark": "paper_tab_qa", "split": "default"} | [{"answer": "SVMs, LR, BIBREF2", "type": "extractive"}, {"answer": "SVM INLINEFORM0, SVM INLINEFORM1, LR INLINEFORM2, MaxEnt", "type": "extractive"}] | [{"raw_evidence": ["Experimental results Table TABREF9 illustrates the performance of the models for the different data representations. The upper part of the Table summarizes the performance of the baselines. The entry “Balikas et al.” stands for the winning system of the 2016 edition of the challenge BIBREF2 , which ... |
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