You will be shown sections from a scientific research paper, together with a question about the paper. Paragraphs in the paper are separated by newlines. Your task is to answer the question based on the contents of the paper.

Paper:
----------------------------------------
Embracing data abundance: BookTest Dataset for Reading Comprehension

There is a practically unlimited amount of natural language data available. Still, recent work in text comprehension has focused on datasets which are small relative to current computing possibilities. This article is making a case for the community to move to larger data and as a step in that direction it is proposing the BookTest, a new dataset similar to the popular Children's Book Test (CBT), however more than 60 times larger. We show that training on the new data improves the accuracy of our Attention-Sum Reader model on the original CBT test data by a much larger margin than many recent attempts to improve the model architecture. On one version of the dataset our ensemble even exceeds the human baseline provided by Facebook. We then show in our own human study that there is still space for further improvement.

Task Description
A natural way of testing a reader's comprehension of a text is to ask her a question the answer to which can be deduced from the text. Hence the task we are trying to solve consists of answering a cloze-style question, the answer to which depends on the understanding of a context document provided with the question. The model is also provided with a set of possible answers from which the correct one is to be selected. This can be formalized as follows:
The training data consist of tuples INLINEFORM0 , where INLINEFORM1 is a question, INLINEFORM2 is a document that contains the answer to question INLINEFORM3 , INLINEFORM4 is a set of possible answers and INLINEFORM5 is the ground-truth answer. Both INLINEFORM6 and INLINEFORM7 are sequences of words from vocabulary INLINEFORM8 . We also assume that all possible answers are words from the vocabulary, that is INLINEFORM9 . In the CBT and CNN/Daily Mail datasets it is also true that the ground-truth answer INLINEFORM10 appears in the document. This is exploited by many machine learning models BIBREF2 , BIBREF4 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF10 , BIBREF11 , BIBREF12 , however some do not explicitly depend on this property BIBREF1 , BIBREF3 , BIBREF5 , BIBREF9 

Current Landscape
We will now briefly review what datasets for text comprehension have been published up to date and look at models which have been recently applied to solving the task we have just described.

Datasets
A crucial condition for applying deep-learning techniques is to have a huge amount of data available for training. For question answering this specifically means having a large number of document-question-answer triples available. While there is an unlimited amount of text available, coming up with relevant questions and the corresponding answers can be extremely labour-intensive if done by human annotators. There were efforts to provide such human-generated datasets, e.g. Microsoft's MCTest BIBREF17 , however their scale is not suitable for deep learning without pre-training on other data BIBREF18 (such as using pre-trained word embedding vectors).
Google DeepMind managed to avoid this scale issue with their way of generating document-question-answer triples automatically, closely followed by Facebook with a similar method. Let us now briefly introduce the two resulting datasets whose properties are summarized in Table TABREF8 .
These two datasets BIBREF1 exploit a useful feature of online news articles – many articles include a short summarizing sentence near the top of the page. Since all information in the summary sentence is also presented in the article body, we get a nice cloze-style question about the article contents by removing a word from the short summary.
The dataset's authors also replaced all named entities in the dataset by anonymous tokens which are further shuffled for each new batch. This forces the model to rely solely on information from the context document, not being able to transfer any meaning of the named entities between documents.
This restricts the task to one specific aspect of context-dependent question answering which may be useful however it moves the task further from the real application scenario, where we would like the model to use all information available to answer questions. Furthermore Chen et al. BIBREF5 have suggested that this can make about 17% of the questions unanswerable even by humans. They also claim that more than a half of the question sentences are mere paraphrases or exact matches of a single sentence from the context document. This raises a question to what extent the dataset can test deeper understanding of the articles.
The Children's Book Test BIBREF2 uses a different source - books freely available thanks to Project Gutenberg. Since no summary is available, each example consists of a context document formed from 20 consecutive sentences from the story together with a question formed from the subsequent sentence.
The dataset comes in four flavours depending on what type of word is omitted from the question sentence. Based on human evaluation done in BIBREF2 it seems that NE and CN are more context dependent than the other two types – prepositions and verbs. Therefore we (and all of the recent publications) focus only on these two word types.
Several new datasets related to the (now almost standard) ones above emerged recently. We will now briefly present them and explain how the dataset we are introducing in this article differs from them.
The LAMBADA dataset BIBREF19 is designed to measure progress in understanding common-sense questions about short stories that can be easily answered by humans but cannot be answered by current standard machine-learning models (e.g. plain LSTM language models). This dataset is useful for measuring the gap between humans and machine learning algorithms. However, by contrast to our BookTest dataset, it will not allow us to track progress towards the performance of the baseline systems or on examples where machine learning may show super-human performance. Also LAMBADA is just a diagnostic dataset and does not provide ready-to-use question-answering training data, just a plain-text corpus which may moreover include copyrighted books making its use potentially problematic for some purposes. We are providing ready training data consisting of copyright-free books only.
The SQuAD dataset BIBREF20 based on Wikipedia and the Who-did-What dataset BIBREF21 based on Gigaword news articles are factoid question-answering datasets where a multi-word answer should be extracted from a context document. This is in contrast to the previous datasets, including CNN/DM, CBT, LAMBADA and our new dataset, which require only single-word answers. Both these datasets however provide less than 130,000 training questions, two orders of magnitude less than our dataset does.
The Story Cloze Test BIBREF22 provides a crowd-sourced corpus of 49,255 commonsense stories for training and 3,744 testing stories with right and wrong endings. Hence the dataset is again rather small. Similarly to LAMBADA, the Story Cloze Test was designed to be easily answerable by humans.
In the WikiReading BIBREF23 dataset the context document is formed from a Wikipedia article and the question-answer pair is taken from the corresponding WikiData page. For each entity (e.g. Hillary Clinton), WikiData contain a number of property-value pairs (e.g. place of birth: Chicago) which form the datasets's question-answer pairs. The dataset is certainly relevant to the community, however the questions are of very limited variety with only 20 properties (and hence unique questions) covering INLINEFORM0 of the dataset. Furthermore many of the frequent properties are mentioned at a set spot within the article (e.g. the date of birth is almost always in brackets behind the name of a person) which may make the task easier for machines. We are trying to provide a more varied dataset.
Although there are several datasets related to task we are aiming to solve, they differ sufficiently for our dataset to bring new value to the community. Its biggest advantage is its size which can furthermore be easily upscaled without expensive human annotation. Finally while we are emphasizing the differences, models could certainly benefit from as diverse a collection of datasets as possible.

Machine Learning Models
A first major work applying deep-learning techniques to text comprehension was Hermann et al. BIBREF1 . This work was followed by the application of Memory Networks to the same task BIBREF2 . Later three models emerged around the same time BIBREF3 , BIBREF4 , BIBREF5 including our psr model BIBREF4 . The AS Reader inspired several subsequent models that use it as a sub-component in a diverse ensemble BIBREF8 ; extend it with a hierarchical structure BIBREF6 , BIBREF24 , BIBREF7 ; compute attention over the context document for every word in the query BIBREF10 or use two-way context-query attention mechanism for every word in the context and the query BIBREF11 that is similar in its spirit to models recently proposed in different domains, e.g. BIBREF25 in information retrieval. Other neural approaches to text comprehension are explored in BIBREF9 , BIBREF12 .

BookTest
Similarly to the CBT, our BookTest dataset is derived from books available through project Gutenberg. We used 3555 copyright-free books to extract CN examples and 10507 books for NE examples, for comparison the CBT dataset was extracted from just 108 books.
When creating our dataset we follow the same procedure as was used to create the CBT dataset BIBREF2 . That is, we detect whether each sentence contains either a named entity or a common noun that already appeared in one of the preceding twenty sentences. This word is then replaced by a gap tag (XXXXX) in this sentence which is hence turned into a cloze-style question. The preceding 20 sentences are used as the context document. For common noun and named entity detection we use the Stanford POS tagger BIBREF27 and Stanford NER BIBREF28 .
The training dataset consists of the original CBT NE and CN data extended with new NE and CN examples. The new BookTest dataset hence contains INLINEFORM0 training examples and INLINEFORM1 tokens.
The validation dataset consists of INLINEFORM0 NE and INLINEFORM1 CN questions. We have one test set for NE and one for CN, each containing INLINEFORM2 examples. The training, validation and test sets were generated from non-overlapping sets of books.
When generating the dataset we removed all editions of books used to create CBT validation and test sets from our training dataset. Therefore the models trained on the BookTest corpus can be evaluated on the original CBT data and they can be compared with recent text-comprehension models utilizing this dataset BIBREF2 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 .

Results
Table TABREF25 shows the accuracy of the psr and other architectures on the CBT validation and test data. The last two rows show the performance of the psr trained on the BookTest dataset; all the other models have been trained on the original CBT training data.
If we take the best psr ensemble trained on CBT as a baseline, improving the model architecture as in BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , continuing to use the original CBT training data, lead to improvements of INLINEFORM0 and INLINEFORM1 absolute on named entities and common nouns respectively. By contrast, inflating the training dataset provided a boost of INLINEFORM2 while using the same model. The ensemble of our models even exceeded the human baseline provided by Facebook BIBREF2 on the Common Noun dataset.
Our model takes approximately two weeks to converge when trained on the BookTest dataset on a single Nvidia Tesla K40 GPU.

Discussion
Embracing the abundance of data may mean focusing on other aspects of system design than with smaller data. Here are some of the challenges that we need to face in this situation.
Firstly, since the amount of data is practically unlimited – we could even generate them on the fly resulting in continuous learning similar to the Never-Ending Language Learning by Carnegie Mellon University BIBREF30 – it is now the speed of training that determines how much data the model is able to see. Since more training data significantly help the model performance, focusing on speeding up the algorithm may be more important than ever before. This may for instance influence the decision whether to use regularization such as dropout which does seem to somewhat improve the model performance, however usually at a cost of slowing down training.
Thanks to its simplicity, the psr seems to be training fast - for example around seven times faster than the models proposed by Chen et al. BIBREF5 . Hence the psr may be particularly suitable for training on large datasets.
The second challenge is how to generalize the performance gains from large data to a specific target domain. While there are huge amounts of natural language data in general, it may not be the case in the domain where we may want to ultimately apply our model.
Hence we are usually not facing a scenario of simply using a larger amount of the same training data, but rather extending training to a related domain of data, hoping that some of what the model learns on the new data will still help it on the original task.
This is highlighted by our observations from applying a model trained on the BookTest to Children's Book Test test data. If we move model training from joint CBT NE+CN training data to a subset of the BookTest of the same size (230k examples), we see a drop in accuracy of around 10% on the CBT test datasets.
Hence even though the Children's Book Test and BookTest datasets are almost as close as two disjoint datasets can get, the transfer is still very imperfect . Rightly choosing data to augment the in-domain training data is certainly a problem worth exploring in future work.
Our results show that given enough data the AS Reader was able to exceed the human performance on CBT CN reported by Facebook. However we hypothesized that the system is still not achieving its full potential so we decided to examine the room for improvement in our own small human study.

Conclusion
Few ways of improving model performance are as solidly established as using more training data. Yet we believe this principle has been somewhat neglected by recent research in text comprehension. While there is a practically unlimited amount of data available in this field, most research was performed on unnecessarily small datasets.
As a gentle reminder to the community we have shown that simply infusing a model with more data can yield performance improvements of up to INLINEFORM0 where several attempts to improve the model architecture on the same training data have given gains of at most INLINEFORM1 compared to our best ensemble result. Yes, experiments on small datasets certainly can bring useful insights. However we believe that the community should also embrace the real-world scenario of data abundance.
The BookTest dataset we are proposing gives the reading-comprehension community an opportunity to make a step in that direction.

Table 1: Statistics on the 4 standard text comprehension datasets and our new BookTest dataset introduced in this paper. CBT CN stands for CBT Common Nouns and CBT NE stands for CBT Named Entites. Statistics were taken from (Hermann et al., 2015) and the statistics provided with the CBT data set.

Table 2: Results of various architectures on the CBT test datasets.

Table 3: Accuracy of human participants on examples from validation set that were previously incorrectly answered by our system trained only on the BookTest data.
----------------------------------------

Question: How large are the improvements of the Attention-Sum Reader model when using the BookTest dataset?

To answer the question, format your response as a `json` object with two fields:

"answer": A string providing a succinct answer to the question, in your own words.
"evidence": An array of strings. Each entry should be a full paragraph from the paper. Together, the evidence should serve as a justification for the answer.

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

{
  "answer": "BERT and RoBERTa."
  "evidence": ["We compare our approach against two baselines. In Table 1, we compare against BERT. In Table 2, we compare against RoBERTa. Our findings indicate that our approach improves over both baeslines..."]
}

The "answer" field should be roughly 229 characters in length.

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".

To repeat, the question is: How large are the improvements of the Attention-Sum Reader model when using the BookTest dataset?

Answer JSON object:
{"answer": "Answer with content missing: (Table 2) Accuracy of best AS reader results including ensembles are 78.4 and 83.7 when trained on BookTest compared to 71.0 and 68.9 when trained on CBT for Named endity and Common noun respectively.", "evidence": ["Table TABREF25 shows the accuracy of the psr and other architectures on the CBT validation and test data. The last two rows show the performance of the psr trained on the BookTest dataset; all the other models have been trained on the original CBT training data."]}