Title: NEWSQA: A MACHINE COMPREHENSION DATASET

Abstract: We present NewsQA, a challenging machine comprehension dataset of over 100,000 question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting in spans of text from the corresponding articles. We collect this dataset through a fourstage process designed to solicit exploratory questions that require reasoning. A thorough analysis confirms that NewsQA demands abilities beyond simple word matching and recognizing entailment. We measure human performance on the dataset and compare it to several strong neural models. The performance gap between humans and machines (0.198 in F1) indicates that significant progress can be made on NewsQA through future research. The dataset is freely available at datasets.maluuba.com/NewsQA.

1. INTRODUCTION:

Almost all human knowledge is recorded in the language of text. As such, comprehension of written language by machines, at a near-human level, would enable a broad class of artificial intelligence applications. In human students we evaluate reading comprehension by posing questions based on a text passage and then assessing a student's answers. Such comprehension tests are appealing because they are objectively gradable and may measure a range of important abilities, from basic understanding to causal reasoning to inference (Richardson et al., 2013) . To teach literacy to machines, the research community has taken a similar approach with machine comprehension (MC).

Recent years have seen the release of a host of MC datasets. Generally, these consist of (document, question, answer) triples to be used in a supervised learning framework. Existing datasets vary in size, difficulty, and collection methodology; however, as pointed out by Rajpurkar et al. (2016) , most suffer from one of two shortcomings: those that are designed explicitly to test comprehension (Richardson et al., 2013) are too small for training data-intensive deep learning models, while those that are sufficiently large for deep learning (Hermann et al., 2015; Hill et al., 2016; Bajgar et al., 2016) are generated synthetically, yielding questions that are not posed in natural language and that may not test comprehension directly (Chen et al., 2016) . More recently, Rajpurkar et al. (2016) sought to overcome these deficiencies with their crowdsourced dataset, SQuAD.

Here we present a challenging new largescale dataset for machine comprehension: NewsQA. NewsQA contains 119,633 natural language questions posed by crowdworkers on 12,744 news articles from CNN. Answers to these questions consist in spans of text within the corresponding article highlighted by a distinct set of crowdworkers. To build NewsQA we utilized a four-stage collection process designed to encourage exploratory, curiosity-based questions that reflect human information seeking. CNN articles were chosen as the source material because they have been used in the past (Hermann et al., 2015) and, in our view, machine comprehension systems are particularly suited to high-volume, rapidly changing information sources like news.

As Trischler et al. (2016a) , Chen et al. (2016) , and others have argued, it is important for datasets to be sufficiently challenging to teach models the abilities we wish them to learn. Thus, in line with Richardson et al. (2013) , our goal with NewsQA was to construct a corpus of questions that necessitates reasoning mechanisms, such as synthesis of information across different parts of an article. We designed our collection methodology explicitly to capture such questions.

The challenging characteristics of NewsQA that distinguish it from most previous comprehension tasks are as follows:

1. Answers are spans of arbitrary length within an article, rather than single words or entities.

2. Some questions have no answer in the corresponding article (the null span).

3. There are no candidate answers from which to choose. 4. Our collection process encourages lexical and syntactic divergence between questions and answers.

5. A significant proportion of questions requires reasoning beyond simple word-and contextmatching (as shown in our analysis).

In this paper we describe the collection methodology for NewsQA, provide a variety of statistics to characterize it and contrast it with previous datasets, and assess its difficulty. In particular, we measure human performance and compare it to that of two strong neural-network baselines. Unsurprisingly, humans significantly outperform the models we designed and assessed, achieving an F1 score of 0.694 versus 0.496 for the best-performing machine. We hope that this corpus will spur further advances on the challenging task of machine comprehension.

2. RELATED DATASETS:

NewsQA follows in the tradition of several recent comprehension datasets. These vary in size, difficulty, and collection methodology, and each has its own distinguishing characteristics. We agree with Bajgar et al. (2016) who have said "models could certainly benefit from as diverse a collection of datasets as possible." We discuss this collection below.

2.1. MCTEST:

MCTest (Richardson et al., 2013) is a crowdsourced collection of 660 elementary-level children's stories with associated questions and answers. The stories are fictional, to ensure that the answer must be found in the text itself, and carefully limited to what a young child can understand. Each question comes with a set of 4 candidate answers that range from single words to full explanatory sentences. The questions are designed to require rudimentary reasoning and synthesis of information across sentences, making the dataset quite challenging. This is compounded by the dataset's size, which limits the training of expressive statistical models. Nevertheless, recent comprehension models have performed well on MCTest (Sachan et al., 2015; Wang et al., 2015) , including a highly structured neural model (Trischler et al., 2016a) . These models all rely on access to the small set of candidate answers, a crutch that NewsQA does not provide.

2.2. CNN/DAILY MAIL:

The CNN/Daily Mail corpus (Hermann et al., 2015) consists of news articles scraped from those outlets with corresponding cloze-style questions. Cloze questions are constructed synthetically by deleting a single entity from abstractive summary points that accompany each article (written presumably by human authors). As such, determining the correct answer relies mostly on recognizing textual entailment between the article and the question. The named entities within an article are identified and anonymized in a preprocessing step and constitute the set of candidate answers; contrast this with NewsQA in which answers often include longer phrases and no candidates are given.

Because the cloze process is automatic, it is straightforward to collect a significant amount of data to support deep-learning approaches: CNN/Daily Mail contains about 1.4 million question-answer pairs. However, Chen et al. (2016) demonstrated that the task requires only limited reasoning and, in fact, performance of the strongest models (Kadlec et al., 2016; Trischler et al., 2016b; Sordoni et al., 2016) nearly matches that of humans.

2.3. CHILDREN'S BOOK TEST:

The Children's Book Test (CBT) (Hill et al., 2016) This is an extension to the named-entity and common-noun strata of CBT that increases their size by over 60 times. Bajgar et al. (2016) demonstrate that training on the augmented dataset yields a model (Kadlec et al., 2016 ) that matches human performance on CBT. This is impressive and suggests that much is to be gained from more data, but we repeat our concerns about the relevance of story prediction as a comprehension task. We also wish to encourage more efficient learning from less data.

2.5. SQUAD:

The comprehension dataset most closely related to NewsQA is SQuAD (Rajpurkar et al., 2016) . It consists of natural language questions posed by crowdworkers on paragraphs from high-PageRank Wikipedia articles. As in NewsQA, each answer consists of a span of text from the related paragraph and no candidates are provided. Despite the effort of manual labelling, SQuAD's size is significant and amenable to deep learning approaches: 107,785 question-answer pairs based on 536 articles.

SQuAD is a challenging comprehension task in which humans far outperform machines. The authors measured human accuracy at 0.905 in F1 (we measured human F1 at 0.807 using a different methodology), whereas at the time of the writing, the strongest published model to date achieves only 0.700 in F1 (Wang & Jiang, 2016b) .

3. COLLECTION METHODOLOGY:

We collected NewsQA through a four-stage process: article curation, question sourcing, answer sourcing, and validation. We also applied a post-processing step with answer agreement consolidation and span merging to enhance the usability of the dataset.

3.1. ARTICLE CURATION:

We retrieve articles from CNN using the script created by Hermann et al. (2015) for CNN/Daily Mail. From the returned set of 90,266 articles, we select 12,744 uniformly at random. These cover a wide range of topics that includes politics, economics, and current events. Articles are partitioned at random into a training set (90%), a development set (5%), and a test set (5%).

3.2. QUESTION SOURCING:

It was important to us to collect challenging questions that could not be answered using straightforward word-or context-matching. Like Richardson et al. (2013) we want to encourage reasoning in comprehension models. We are also interested in questions that, in some sense, model human curiosity and reflect actual human use-cases of information seeking. Along a similar line, we consider it an important (though as yet overlooked) capacity of a comprehension model to recognize when given information is inadequate, so we are also interested in questions that may not have sufficient evidence in the text. Our question sourcing stage was designed to solicit questions of this nature, and deliberately separated from the answer sourcing stage for the same reason.

Questioners (a distinct set of crowdworkers) see only a news article's headline and its summary points (also available from CNN); they do not see the full article itself. They are asked to formulate a question from this incomplete information. This encourages curiosity about the contents of the full article and prevents questions that are simple reformulations of sentences in the text. It also increases the likelihood of questions whose answers do not exist in the text. We reject questions that have significant word overlap with the summary points to ensure that crowdworkers do not treat the summaries as mini-articles, and further discouraged this in the instructions. During collection each Questioner is solicited for up to three questions about an article. They are provided with positive and negative examples to prompt and guide them (detailed instructions are shown in Figure 3 ).

3.3. ANSWER SOURCING:

A second set of crowdworkers (Answerers) provide answers. Although this separation of question and answer increases the overall cognitive load, we hypothesized that unburdening Questioners in this way would encourage more complex questions. Answerers receive a full article along with a crowdsourced question and are tasked with determining the answer. They may also reject the question as nonsensical, or select the null answer if the article contains insufficient information. Answers are submitted by clicking on and highlighting words in the article while instructions encourage the set of answer words to consist in a single continuous span (again, we give an example prompt in the Appendix). For each question we solicit answers from multiple crowdworkers (avg. 2.73) with the aim of achieving agreement between at least two Answerers.

3.4. VALIDATION:

Crowdsourcing is a powerful tool but it is not without peril (collection glitches; uninterested or malicious workers). To obtain a dataset of the highest possible quality we use a validation process that mitigates some of these issues. In validation, a third set of crowdworkers sees the full article, a question, and the set of unique answers to that question. We task these workers with choosing the best answer from the candidate set or rejecting all answers. Each article-question pair is validated by an average of 2.48 crowdworkers. Validation was used on those questions without answer-agreement after the previous stage, amounting to 43.2% of all questions.

3.5. ANSWER MARKING AND CLEANUP:

After validation, 86.0% of all questions in NewsQA have answers agreed upon by at least two separate crowdworkers-either at the initial answer sourcing stage or in the top-answer selection. This improves the dataset's quality. We choose to include the questions without agreed answers in the corpus also, but they are specially marked. Such questions could be treated as having the null answer and used to train models that are aware of poorly posed questions.

As a final cleanup step we combine answer spans that are less than 3 words apart (punctuation is discounted). We find that 5.68% of answers consist in multiple spans, while 71.3% of multi-spans are within the 3-word threshold. Looking more closely at the data reveals that the multi-span answers often represent lists. These may present an interesting challenge for comprehension models moving forward.

4. DATA ANALYSIS:

We provide a thorough analysis of NewsQA to demonstrate its challenge and its usefulness as a machine comprehension benchmark. The analysis focuses on the types of answers that appear in the dataset and the various forms of reasoning required to solve it.1 

4.1 ANSWER TYPES Following Rajpurkar et al. (2016) , we categorize answers based on their linguistic type (see Table 1 ). This categorization relies on Stanford CoreNLP to generate constituency parses, POS tags, and NER The proportions in Table 1 only account for cases when an answer span exists. The complement of this set comprises questions with an agreed null answer (9.5% of the full corpus) and answers without agreement after validation (4.5% of the full corpus).

4.2. REASONING TYPES:

The forms of reasoning required to solve NewsQA directly influence the abilities that models will learn from the dataset. We stratified reasoning types using a variation on the taxonomy presented by Chen et al. (2016) in their analysis of the CNN/Daily Mail dataset. Types are as follows, in ascending order of difficulty:

1. Word Matching: Important words in the question exactly match words in the immediate context of an answer span such that a keyword search algorithm could perform well on this subset.

2.:

Paraphrasing: A single sentence in the article entails or paraphrases the question. Paraphrase recognition may require synonymy and word knowledge.

3.:

Inference: The answer must be inferred from incomplete information in the article or by recognizing conceptual overlap. This typically draws on world knowledge.

4.. Synthesis::

The answer can only be inferred by synthesizing information distributed across multiple sentences.

5. Ambiguous/Insufficient: The question has no answer or no unique answer in the article.

For both NewsQA and SQuAD, we manually labelled 1,000 examples (drawn randomly from the respective development sets) according to these types and compiled the results in Table 2 . Some examples fall into more than one category, in which case we defaulted to the more challenging type. We can see from the table that word matching, the easiest type, makes up the largest subset in both datasets (32.7% for NewsQA and 39.8% for SQuAD). Paraphrasing constitutes a much larger proportion in SQuAD than in NewsQA (34.3% vs 27.0%), possibly a result from the explicit encouragement of lexical variety in SQuAD question sourcing. However, NewsQA significantly outnumbers SQuAD on the distribution of the more difficult forms of reasoning: synthesis and inference make up 33.9% of the data in contrast to 20.5% in SQuAD.

5. BASELINE MODELS:

We test the performance of three comprehension systems on NewsQA: human data analysts and two neural models. The first neural model is the match-LSTM (mLSTM) system of Wang & Jiang (2016b). The second is a model of our own design that is computationally cheaper. We describe these models below but omit the personal details of our analysts. Implementation details of the models are described in Appendix A.

5.1. MATCH-LSTM:

There are three stages involved in the mLSTM model. First, LSTM networks encode the document and question (represented by GloVe word embeddings (Pennington et al., 2014) ) as sequences of hidden states. Second, an mLSTM network (Wang & Jiang, 2016a) compares the document encodings with the question encodings. This network processes the document sequentially and at each token uses an attention mechanism to obtain a weighted vector representation of the question; the weighted combination is concatenated with the encoding of the current token and fed into a standard LSTM. Finally, a Pointer Network uses the hidden states of the mLSTM to select the boundaries of the answer span. We refer the reader to Wang & Jiang (2016a; b) for full details. At the time of writing, mLSTM is state-of-the-art on SQuAD (see Table 3 ) so it is natural to test it further on NewsQA.

5.2. THE BILINEAR ANNOTATION RE-ENCODING BOUNDARY (BARB) MODEL:

The match-LSTM is computationally intensive since it computes an attention over the entire question at each document token in the recurrence. To facilitate faster experimentation with NewsQA we developed a lighter-weight model (BARB) that achieves similar results on SQuAD2 . Our model consists in four stages:

Encoding All words in the document and question are mapped to real-valued vectors using the GloVe embedding matrix W ∈ R |V |×d . This yields d 1 , . . . , d n ∈ R d and q 1 , . . . , q m ∈ R d . A bidirectional GRU network (Bahdanau et al., 2015) takes in d i and encodes contextual states h i ∈ R D1 for the document. The same encoder is applied to q j to derive contextual states k j ∈ R D1 for the question. 3

Bilinear Annotation Next we compare the document and question encodings using a set of C bilinear transformations,

g ij = h T i T [1:C] k j , T c ∈ R D1×D1 , g ij ∈ R C , which we use to produce an (n × m × C)-dimensional tensor of annotation scores, G = [g ij ].

We take the maximum over the question-token (second) dimension and call the columns of the resulting matrix g i ∈ R C . We use this matrix as an annotation over the document word dimension. Contrasting the multiplicative application of attention vectors, this annotation matrix is to be concatenated to the encoder RNN input in the re-encoding stage.

Re-encoding For each document word, the input of the re-encoding RNN (another biGRU network) consists of three components: the document encodings h i , the annotation vectors g i , and a binary feature q i indicating whether the document word appears in the question. The resulting vectors f i = [h i ; g i ; q i ] are fed into the re-encoding RNN to produce D 2 -dimensional encodings e i as input in the boundary-pointing stage.

Boundary pointing Finally, we search for the boundaries of the answer span using a convolutional network (in a process similar to edge detection). Encodings e i are arranged in matrix E ∈ R D2×n . E is convolved with a bank of n f filters, F k ∈ R D2×w , where w is the filter width, k indexes the different filters, and indexes the layer of the convolutional network. Each layer has the same number of filters of the same dimensions. We add a bias term and apply a nonlinearity (ReLU) following each convolution, with the result an (n f × n)-dimensional matrix B .

We use two convolutional layers in the boundary-pointing stage. Given B 1 and B 2 , the answer span's start-and end-location probabilities are computed using p(s) ∝ exp v T s B 1 + b s and p(e) ∝ exp v T e B 2 + b e , respectively. We also concatenate p(s) to the input of the second convolutional layer (along the n f -dimension) so as to condition the end-boundary pointing on the start-boundary. Vectors v s , v e ∈ R n f and scalars b s , b e ∈ R are trainable parameters.

We also provide an intermediate level of "guidance" to the annotation mechanism by first reducing the feature dimension C in G with mean-pooling, then maximizing the softmax probabilities in the resulting (n-dimensional) vector corresponding to the answer word positions in each document. This auxiliary task is observed empirically to improve performance.

6 EXPERIMENTS4 

6.1. HUMAN EVALUATION:

We tested four English speakers (three native and one near-native) on a total of 1,000 questions from the NewsQA development set. As given in Table 3 , they averaged 0.694 in F1, which likely represents a ceiling for machine performance. Our students' exact match (EM) scores are relatively low at 0.465. This is because in many cases there are multiple ways to select semantically equivalent answers, e.g., "1996" versus "in 1996". We also compared human performance on the answers that had agreement with and without validation, finding a difference of only 1.4 percentage points F1. This suggests our validation stage yields good-quality answers.

The original SQuAD evaluation of human performance compares separate answers given by crowdworkers; for a closer comparison with NewsQA, we replicated our human test on the same number of validation data (1,000). We measured their answers against the second group of crowdsourced responses in SQuAD's development set, as in Rajpurkar et al. (2016) . Our students scored 0.807 in F1.

6.2. MODEL PERFORMANCE:

Performance of the baseline models and humans is measured by EM and F1 with the official evaluation script from SQuAD and listed in Table 3 . Unless otherwise stated, hyperparameters are determined by hyperopt (Appendix A). The gap between human and machine performance on NewsQA is a striking 0.198 points F1 -much larger than the gap on SQuAD (0.098) under the same human evaluation scheme. The gaps suggest a large margin for improvement with automated methods. the model is better at pointing to named entities compared to other types of answers. The reasoningtype stratification, on the other hand, shows that questions requiring inference and synthesis are, not surprisingly, more difficult for the model. Consistent with observations in Table 3 , stratified performance on NewsQA is significantly lower than on SQuAD. The difference is smallest on word matching and largest on synthesis. We postulate that the longer stories in NewsQA make synthesizing information from separate sentences more difficult, since the relevant sentences may be farther apart. This requires the model to track longer-term dependencies.

6.3. SENTENCE-LEVEL SCORING:

We propose a simple sentence-level subtask as an additional quantitative demonstration of the relative difficulty of NewsQA. Given a document and a question, the goal is to find the sentence containing the answer span. We hypothesize that simple techniques like word-matching are inadequate to this task owing to the more involved reasoning required by NewsQA.

We employ a technique that resembles inverse document frequency (idf ), which we call inverse sentence frequency (isf ). Given a sentence S i from an article and its corresponding question Q, the isf score is given by the sum of the idf scores of the words common to S i and Q (each sentence is treated as a document for the idf computation). The sentence with the highest isf is taken as the answer sentence S * , that is, S * = arg max i w∈Si∩Q isf (w).

The isf method achieves an impressive 79.4% sentence-level accuracy on SQuAD's development set but only 35.4% accuracy on NewsQA's development set, highlighting the comparative difficulty of the latter. To eliminate the difference in article length as a possible cause of the performance difference, we also artificially increased the article lengths in SQuAD by concatenating adjacent SQuAD articles from the same Wikipedia document. Accuracy decreases as expected with the increased SQuAD article length, yet remains significantly higher than that on NewsQA with comparable or even larger article length (Table 4 ). 

7. CONCLUSION:

We have introduced a challenging new comprehension dataset: NewsQA. We collected the 100,000+ examples of NewsQA using teams of crowdworkers, who variously read CNN articles or highlights, posed questions about them, and determined answers. Our methodology yields diverse answer types and a significant proportion of questions that require some reasoning ability to solve. This makes the corpus challenging, as confirmed by the large performance gap between humans and deep neural models (0.198 in F1) . By its size and complexity, NewsQA makes a significant extension to the existing body of comprehension datasets. We hope that our corpus will spur further advances in machine comprehension and guide the development of literate artificial intelligence.

:

Additional statistics are available at http://datasets.maluuba.com/NewsQA/stats.

With the configurations for the results reported in Section 6.2, one epoch of training on NewsQA takes about

3.9k seconds for BARB and 8.1k seconds for mLSTM. 3 A bidirectional GRU concatenates the hidden states of two GRU networks running in opposite directions. Each of these has hidden size 1 2 D1.

All experiments in this section use the subset of NewsQA dataset with answer agreements (92,549 samples for training,

5,166 for validation, and 5,126 for testing). We leave the challenge of identifying the unanswerable questions for future work.

https://github.com/hyperopt/hyperopt

Figures and Tables:

Figure 1 stratifies model (BARB) performance according to answer type (left) and reasoning type (right) as defined in Sections 4.1 and 4.2, respectively. The answer-type stratification suggests that

Figure 1: Left: BARB performance (F1 and EM) stratified by answer type on the full development set of NewsQA. Right: BARB performance (F1) stratified by reasoning type on the human-assessed subset on both NewsQA and SQuAD. Error bars indicate performance differences between BARB and human annotators.

The variety of answer types appearing in NewsQA, with proportion statistics and examples.

Table content: <table><tr><td>Answer type</td><td>Example</td><td>Proportion (%)</td></tr><tr><td>Date/Time</td><td>March 12, 2008</td><td>2.9</td></tr><tr><td>Numeric</td><td>24.3 million</td><td>9.8</td></tr><tr><td>Person</td><td>Ludwig van Beethoven</td><td>14.8</td></tr><tr><td>Location</td><td>Torrance, California</td><td>7.8</td></tr><tr><td>Other Entity</td><td>Pew Hispanic Center</td><td>5.8</td></tr><tr><td colspan="2">Common Noun Phrase federal prosecutors</td><td>22.2</td></tr><tr><td>Adjective Phrase</td><td>5-hour</td><td>1.9</td></tr><tr><td>Verb Phrase</td><td>suffered minor damage</td><td>1.4</td></tr><tr><td>Clause Phrase</td><td>trampling on human rights</td><td>18.3</td></tr><tr><td>Prepositional Phrase</td><td>in the attack</td><td>3.8</td></tr><tr><td>Other</td><td>nearly half</td><td>11.2</td></tr><tr><td colspan="3">tags for answer spans (see Rajpurkar et al. (2016) for more details). From the table we see that the</td></tr><tr><td colspan="3">majority of answers (22.2%) are common noun phrases. Thereafter, answers are fairly evenly spread</td></tr><tr><td colspan="3">among the clause phrase (18.3%), person (14.8%), numeric (9.8%), and other (11.2%) types. Clearly,</td></tr><tr><td colspan="2">answers in NewsQA are linguistically diverse.</td><td/></tr></table>

Reasoning mechanisms needed to answer questions. For each we show an example question with the sentence that contains the answer span, with words relevant to the reasoning type in bold, and the corresponding proportion in the human-evaluated subset of both NewsQA and SQuAD (1,000 samples each). Who is the struggle between in Rwanda? S: The struggle pits ethnic Tutsis, supported by Rwanda, against ethnic Hutu, backed by Congo. Who drew inspiration from presidents? S: Rudy Ruiz says the lives of US presidents can make them positive role models for students. Where is Brittanee Drexel from? S: The mother of a 17-year-old Rochester, New York high school student ... says she did not give her daughter permission to go on the trip. Brittanee Marie Drexel's mom says... Whose mother is moving to the White House? S: ... Barack Obama's mother-in-law, Marian Robinson, will join the Obamas at the family's private quarters at 1600 Pennsylvania Avenue. [Michelle is never mentioned]

Table content: <table><tr><td>Reasoning</td><td>Example</td></tr></table>

Performance of several methods and humans on the SQuAD and NewsQA datasets. Superscript 1 indicates the results are taken fromRajpurkar et al. (2016), and 2 fromWang & Jiang  (2016b).

Table content: <table><tr><td>SQuAD</td><td colspan="2">Exact Match</td><td>F1</td><td/><td>NewsQA</td><td/><td colspan="2">Exact Match</td><td>F1</td><td/><td/></tr><tr><td>Model Random 1 mLSTM 2 BARB</td><td colspan="4">Dev 0.11 0.591 0.595 0.700 0.703 Test Dev Test 0.13 0.41 0.43 0.591 -0.709 -</td><td colspan="6">Model Random mLSTM 0.344 0.349 0.496 0.500 Dev Test Dev Test 0.00 0.00 0.30 0.30 BARB 0.361 0.341 0.496 0.482</td><td/></tr><tr><td colspan="5">Human 1 Human (ours) 0.650 0.803 0.770 0.905 0.868 -0.807 -</td><td>Human</td><td colspan="2">0.465</td><td>-</td><td>0.694</td><td>-</td><td/></tr><tr><td>Date/time</td><td/><td/><td/><td/><td>Word</td><td/><td/><td/><td/><td/><td/></tr><tr><td>Numeric</td><td/><td/><td/><td/><td>Matching</td><td/><td/><td/><td/><td/><td/></tr><tr><td>Person</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td>Adjective Phrase</td><td/><td/><td/><td/><td>Paraphrasing</td><td/><td/><td/><td/><td/><td/></tr><tr><td>Location</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td>Prepositional Phrase</td><td/><td/><td/><td/><td>Inference</td><td/><td/><td/><td/><td/><td/></tr><tr><td>Common Noun Phrase</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td>Other</td><td/><td/><td/><td/><td>Synthesis</td><td/><td/><td/><td/><td/><td/></tr><tr><td>Other entity</td><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td>Clause Phrase Verb Phrase</td><td/><td/><td/><td>F1 EM</td><td>Ambiguous/ Insufficient</td><td/><td/><td/><td/><td colspan="2">NewsQA SQuAD</td></tr><tr><td>0</td><td>0.2</td><td>0.4</td><td>0.6</td><td>0.8</td><td>0.000</td><td>0.150</td><td>0.300</td><td>0.450</td><td>0.600</td><td>0.750</td><td>0.900</td></tr></table>

Sentence-level accuracy on artificially-lengthened SQuAD documents.

Table content: <table><tr><td/><td colspan="2">SQuAD</td><td/><td/><td/><td>NewsQA</td></tr><tr><td># documents</td><td>1</td><td>3</td><td>5</td><td>7</td><td>9</td><td>1</td></tr><tr><td>Avg # sentences</td><td colspan="5">4.9 14.3 23.2 31.8 40.3</td><td>30.7</td></tr><tr><td colspan="6">isf 79.6 74.9 73.0 72.3 71.0</td><td>35.4</td></tr></table>

Peer review (iclr2017):
Summary: The paper proposes a novel machine comprehension dataset called NEWSQA. The dataset consists of over 100,000 question answer pairs based on over 10,000 news articles from CNN. The paper analyzes the different types of answers and the different types of reasoning required to answer questions in the dataset. The paper evaluates human performance and the performance of two baselines on the dataset and compares them with the performance on SQuAD dataset. Strengths: 1. The paper presents a large scale dataset for machine comprehension. 2. The question collection method seems reasonable to collect exploratory questions. Having an answer validation step is desirable. 3. The paper proposes a novel (computationally more efficient) implementation of the match-LSTM model. Weaknesses: 1. The human evaluation presented in the paper is not satisfactory because the human performance is reported on a very small subset (200 questions). So, it seems unlikely that these 200 questions will provide a reliable measure of the human performance on the entire dataset (which consists of thousands of questions). 2. NEWSQA dataset is very similar to SQuAD dataset in terms of the size of the dataset, the type of dataset -- natural language questions posed by crowdworkers, answers comprising of spans of text from related paragraphs. The paper presents two empirical ways to show that NEWSQA is more challenging than SQuAD -- 1) the gap between human and machine performance in NEWSQA is larger than that in SQuAD. However, since the human performance numbers are reported on very small subset, these trends might not carry over when human performance is computed on all of the dataset. 2) the sentence-level accuracy on SQuAD is higher than that in NEWSQA. However, as the paper mentions, the differences in accuracies could likely be due to different lengths of documents in the two datasets. So, even this measure does not truly reflect that SQuAD is less challenging than NEWSQA. So, it is not clear if NEWSQA is truly more challenging than SQuAD. 3. Authors mention that BARB is computationally more efficient and faster compared to match-LSTM. However, the paper does not report how much faster BARB is compared to match-LSTM. 4. On page 7, under *Boundary pointing* paragraph, the paper should clarify what *s* in *n_s* refers to. Review summary: While the dataset collection method seems interesting and promising, I would be more convinced after I see the following -- 1. Human performance on all (or significant percentage of the dataset). 2. An empirical study that fairly shows that NEWSQA is more challenging (or better in some other way) than SQuAD.