You will be shown sections from a scientific research paper, together with a question about the paper. Your task is to answer the question based on the contents of the paper. Your response should be a `json` object with two fields:

"answer": An array of strings extracted directly from the paper which, collectively, answer the question.
"evidence": An array of strings. Each should be an excerpt from the paper, in which one or more of the extracted answers can be found.

For instance, for the question "What baselines did the authors compare against?", a sample response might be:

{
  "answer": ["BERT", "RoBERTa"],
  "evidence": ["In our experiments, we compare the performance of our model against BERT and RoBERTa."]
}

Do not include any text in your response other than the json.

If the question is unanswerable given the provided excerpts, respond with the single word "null".

Paper: ReviewQA: a relational aspect-based opinion reading dataset

Deep reading models for question-answering have demonstrated promising performance over the last couple of years. However current systems tend to learn how to cleverly extract a span of the source document, based on its similarity with the question, instead of seeking for the appropriate answer. Indeed, a reading machine should be able to detect relevant passages in a document regarding a question, but more importantly, it should be able to reason over the important pieces of the document in order to produce an answer when it is required. To motivate this purpose, we present ReviewQA, a question-answering dataset based on hotel reviews. The questions of this dataset are linked to a set of relational understanding competencies that we expect a model to master. Indeed, each question comes with an associated type that characterizes the required competency. With this framework, it is possible to benchmark the main families of models and to get an overview of what are the strengths and the weaknesses of a given model on the set of tasks evaluated in this dataset. Our corpus contains more than 500.000 questions in natural language over 100.000 hotel reviews. Our setup is projective, the answer of a question does not need to be extracted from a document, like in most of the recent datasets, but selected among a set of candidates that contains all the possible answers to the questions of the dataset. Finally, we present several baselines over this dataset.

Introduction
A large majority of the human knowledge is recorded through text documents. That is why ability for a system to automatically infer information from text without any structured data has become a major challenge. Answering questions about a given document is a relevant proxy task that has been proposed as a way to evaluate the reading ability of a given model. In this configuration, a text document such as a news article, a document from Wikipedia or any type of text is presented to a machine with an associated set of questions. The system is then expected to answer these questions and evaluated by its accuracy on this task. The machine reading framework is very general and we can imagine a large panel of questions that can possibly handle most of the standard natural language processing tasks. For example, the task of named entities recognition can be formulated as a machine reading one where your document is the sentence and the question would be 'What are the named entities mentioned in this sentence?'. These natural language interactions are an important objective for reading systems.
Recently, many datasets have been proposed to build and evaluate reading models BIBREF0 , BIBREF1 . From cloze style questions BIBREF2 to open questions BIBREF3 , from synthetic data BIBREF4 to human written articles BIBREF5 , many styles of documents and questions have been proposed to challenge reading models. The correct answer to the questions proposed in most of these datasets is a span of text of the source document, which can be restricted to a single word in several cases. It means that the answer should explicitly be present in the source document and that the model should be able to locate it.
Different models have already shown superhuman performance on several of these datasets and particularly on the SQuAD dataset composed of Wikipedia articles BIBREF6 , BIBREF7 . However, some limits of such models have been highlighted when they encounter perturbations into the input documents BIBREF8 . Indeed almost all of the state of the art models on the SQuAD dataset suffer from a lack of robustness against adversarial examples. Once the model is trained, a meaningless sentence added at the end of the text document can completely disturb the reading system. Conversely, these adversarial examples do not seem to fool a human reader who will be capable of answering the questions as well as without this perturbation. One possible explanation of this phenomenon is that computers are good at extracting patterns in the document that match the representation of the question. If multiple spans of the documents look similar to the questions, the reader might not be able to decide which one is relevant. Moreover, Wikipedia articles tend to be written with the same standard writing style, factual, unambiguous. Such writing style tends to favor the pattern matching between the questions and the documents. This format of documents/questions has certainly influenced the design of the comprehension models that have been proposed so far. Most of them are composed of stacked attention layers that match question and document representations.
Following concepts proposed in the 20 bAbI tasks BIBREF4 or in the visual question-answering dataset CLEVR BIBREF9 , we think that the challenge, limited to the detection of relevant passages in a document, is only the first step in building systems that truly understand text. The second step is the ability of reasoning with the relevant information extracted from a document. To set up this challenge, we propose to leverage on a hotel reviews corpus that requires reasoning skills to answer natural language questions. The reviews we used have been extracted from TripAdvisor and originally proposed in BIBREF10 , BIBREF11 . In the original data, each review comes with a set of rated aspects among the seventh available: Business service, Check in / Front Desk, Cleanliness, Location, Room, Sleep Quality, Value and for all the reviews an Overall rating. In this articles we propose to exploit these data to create a dataset of question-answering that will challenge 8 competencies of the reader.
Our contributions can be summarized as follow:

Original data
We used a set of reviews extracted from the TripAdvisor website and originally proposed in BIBREF10 and BIBREF11 . This corpus is available at http://www.cs.virginia.edu/~hw5x/Data/LARA/TripAdvisor/TripAdvisorJson.tar.bz2. Each review comes with the name of the associated hotel, a title, an overall rating, a comment and a list of rated aspects. From 0 to 7 aspects, among value, room, location, cleanliness, check-in/front desk, service, business service, can possibly be rated in a review. Figure FIGREF8 displays a review extracted from this dataset.

Relational reasoning competencies
Objective: Starting with the original corpus, we aim at building a machine reading task where natural language questions will challenge the model on its understanding of the reviews. Indeed learning relational reasoning competencies over natural language documents is a major challenge of the current reading models. These original raw data allow us to generate relational questions that can possibly require a global understanding of the comment and reasoning skills to be treated. For example, asking a question like What is the best aspect rated in this comment ? is not an easy question that can be answered without a deep understanding of the review. It is necessary to capture all the aspects mentioned in the text, to predict their rating and finally to select the best one. The tasks and the dataset we propose are publicly available at http://www.europe.naverlabs.com/Blog/ReviewQA-A-novel-relational-aspect-based-opinion-dataset-for-machine-reading
We introduce a list of 8 different competencies that a reading system should master in order to process reviews and text documents in general. These 8 tasks require different competencies and a different level of understanding of the document to be well answered. For instance, detecting if an aspect is mentioned in a review will require less understanding of the review than predicting explicitly the rating of this aspect. Table TABREF10 presents the 8 tasks we have introduced in this dataset with an example of a question that corresponds to each task. We also provide the expected type of the answer (Yes/No question, rating question...). It can be an additional tool to analyze the errors of the readers.

Paraphrase augmentation using backtranslation
In order to generate more paraphrases of the questions, we used a backtranslation method to enrich them. The idea is to use a translation model that will translate our human-generated questions into another language, and then translate them back to English. This double translation will introduce rewordings of the questions that we will be able to integrate into this dataset. This approach has been used in BIBREF7 to perform data augmentation on the training set. For this purpose, we have trained a fairseq BIBREF19 model to translate sentences from English to French and for French to English. In order to preserve the quality of the sentences we have so far, we only keep the most probable translation of each original sentence. Indeed a beam search is used during the translation to predict the most probable translations which mean that we each translation comes with an associated probability. By selecting only the first translations, we almost double the number of questions without degrading the quality of the questions proposed in the dataset.

Models
In this section, we present the performance of four different models on our dataset: a logistic regression and three neural models. The first one is a basic LSTM BIBREF20 , the second a MemN2N BIBREF18 and the third one is a model of our own design. This fourth model reuses the encoding layers of the R-net BIBREF12 and we modify the final layers with a projection layer that will be able to select the answer among the set of candidates instead of pointing the answerer directly into the source document.
Logistic regression: To produce the representation of the input, we concatenate the Bag-Of-Words representation of the document with the Bag-Of-Words representation of the question. It produces an array of size INLINEFORM0 where INLINEFORM1 is the vocabulary size. Then we use a logistic regression to select the most probable answer among the INLINEFORM2 possibilities.
LSTM: We start with a concatenation of the sequence of indexes of the document with the sequence of indexes of the question. Them we feed an LSTM network with this vector and use the final state as the representation of the input. Finally, we apply a logistic regression over this representation to produce the final decision.
End-to-end memory networks: This architecture is based on two different memory cells (input and output) that contain a representation of the document. A controller, initialized with the encoding of the question, is used to calculate an attention between this controller and the representation of the document in the input memory. This attention is them used to re-weight the representation of the document in the output memory. This response from the output memory is them utilized to update the controller. After that, either a matrix is used to project this representation into the answer space either the controller is used to go through an over hop of memory. This architecture allows the model to sequentially look into the initial document seeking for important information regarding the current state of its controller. This model achieves very good performances on the 20 bAbI tasks dataset.
Deep projective reader: This is a model of our own design, largely inspired by the efficient R-net reader BIBREF12 . The overall architecture is composed of 4 stacked layers: an encoding layer, a question/document attention, a self-attention layer and a projection layer. The following paragraphs briefly describe the overall utility of each of these layers.
Encoding: The sentence is tokenized by words. Each token is represented by the concatenation of its embedding vector and the final state of a bidirectional recurrent network over the characters of this word. Finally, another bidirectional RNN on the top of this representation produce the encoding of the document and the question.
Question/document attention: We apply a question/document attention layer that matches the representation of the question with each token of the document individually to output an attention that gives more weight to the important tokens of the document regarding the question.
Self-attention layer: The previous layer has built a question-aware representation of the document. One problem with such representation is that form the moment each token has only a good knowledge of its closest neighbors. To tackle this problem, BIBREF12 have proposed to use a self-attention layer that matches each individual token with all the other tokens of the document. Doing that, each token is now aware of a larger context.
Output layer: A bidirectional RNN is applied on the top of the last layer and we use its final state as the representation of the input. We use a projection matrix to project this representation into the answer space and select the most probable one

Training details
We propose to train these models on the entire set of tasks and them to measure the overall performance and the accuracy of each individual task. In all the models, we use the Adam optimizer BIBREF21 with a learning rate of 0.01 and the batch size is set to 64. All the parameter are initialized from a Gaussian distribution with mean 0 and a standard deviation of 0.01. The dimension of the word embeddings in the projective deep reading model and the LSTM model is 300 and we use Glove pre-trained vectors ( BIBREF22 ). We use a MemN2N with 5 memory hops and a linear start of 5 epochs. The reviews are split by sentence and each memory block corresponds to one sentence. Each sentence is represented by its bag-of-word representation augmented with temporal encoding as it is suggested in BIBREF18 .

Model performance
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 advanced reasoning tasks. Indeed, the task 5 which corresponds to the prediction of the exact rating of an aspect seems to be very challenging for these model. Maybe the tokenization by sentence to create the memory blocks of the MemN2N, which is appropriated in the case of the bAbI tasks, is not a good representation of the documents when it has to handle human generated comments. However, the logistic regression achieves reasonable performance on these tasks, and do not suffer from catastrophic performance on any tasks. Its worst result comes on task 6 and one of the reason is probably that this architecture is not designed to predict a list of answers. On the contrary, the deep projective reader achieves encouraging on this dataset. It outperforms all the other baselines, with very good scores on the first fourth tasks. The question/document and document/document attention layers proposed in BIBREF12 seem once again to produce rich encodings of the inputs which are relevant for our projection layer.

Conclusion
In this paper, we formalize the sentiment analysis task through the framework of machine reading and release ReviewQA, a relational question-answering corpus. This dataset allows evaluating a set of relational reasoning skills through natural language questions. It is composed of a large panel of human-generated questions. Moreover, we propose to augment the dataset with backtranslated reformulations of these questions. Finally, we evaluate 4 models on this dataset, including a projective model of our own design that seems to be a strong baseline for this dataset. We expect that this large dataset will encourage the research community to develop reasoning models and evaluate their models on this set of tasks.

Figure 2: Number of words per review.

Question: Where are the hotel reviews from?
{"answer": ["TripAdvisor"], "evidence": ["Following concepts proposed in the 20 bAbI tasks BIBREF4 or in the visual question-answering dataset CLEVR BIBREF9 , we think that the challenge, limited to the detection of relevant passages in a document, is only the first step in building systems that truly understand text. The second step is the ability of reasoning with the relevant information extracted from a document. To set up this challenge, we propose to leverage on a hotel reviews corpus that requires reasoning skills to answer natural language questions. The reviews we used have been extracted from TripAdvisor and originally proposed in BIBREF10 , BIBREF11 . In the original data, each review comes with a set of rated aspects among the seventh available: Business service, Check in / Front Desk, Cleanliness, Location, Room, Sleep Quality, Value and for all the reviews an Overall rating. In this articles we propose to exploit these data to create a dataset of question-answering that will challenge 8 competencies of the reader."]}