A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task

06/09/2016 ∙ by Danqi Chen, et al. ∙ Stanford University 0

Enabling a computer to understand a document so that it can answer comprehension questions is a central, yet unsolved goal of NLP. A key factor impeding its solution by machine learned systems is the limited availability of human-annotated data. Hermann et al. (2015) seek to solve this problem by creating over a million training examples by pairing CNN and Daily Mail news articles with their summarized bullet points, and show that a neural network can then be trained to give good performance on this task. In this paper, we conduct a thorough examination of this new reading comprehension task. Our primary aim is to understand what depth of language understanding is required to do well on this task. We approach this from one side by doing a careful hand-analysis of a small subset of the problems and from the other by showing that simple, carefully designed systems can obtain accuracies of 73.6 76.6 7-10 task.

READ FULL TEXT VIEW PDF
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

Code Repositories

QA_RNN

Question Answering Using Attentive Reader and Recurrent Neural Networks


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

1 Introduction

Reading comprehension (RC) is the ability to read text, process it, and understand its meaning.222https://en.wikipedia.org/wiki/Reading_comprehension

How to endow computers with this capacity has been an elusive challenge and a long-standing goal of Artificial Intelligence (e.g.,

[Norvig1978]). Genuine reading comprehension involves interpretation of the text and making complex inferences. Human reading comprehension is often tested by asking questions that require interpretive understanding of a passage, and the same approach has been suggested for testing computers [Burges2013].

In recent years, there have been several strands of work which attempt to collect human-labeled data for this task – in the form of document, question and answer triples – and to learn machine learning models directly from it [Richardson et al.2013, Berant et al.2014, Wang et al.2015]

. However, these datasets consist of only hundreds of documents, as the labeled examples usually require considerable expertise and neat design, making the annotation process quite expensive. The subsequent scarcity of labeled examples prevents us from training powerful statistical models, such as deep learning models, and would seem to prevent a system from learning complex textual reasoning capacities.

Recently, researchers at DeepMind [Hermann et al.2015] had the appealing, original idea of exploiting the fact that the abundant news articles of CNN and Daily Mail

are accompanied by bullet point summaries in order to heuristically create large-scale supervised training data for the reading comprehension task. Figure 

1 gives an example. Their idea is that a bullet point usually summarizes one or several aspects of the article. If the computer understands the content of the article, it should be able to infer the missing entity in the bullet point.

Figure 1: An example item from dataset CNN.

This is a clever way of creating supervised data cheaply and holds promise for making progress on training RC models; however, it is unclear what level of reading comprehension is actually needed to solve this somewhat artificial task and, indeed, what statistical models that do reasonably well on this task have actually learned.

In this paper, our aim is to provide an in-depth and thoughtful analysis of this dataset and what level of natural language understanding is needed to do well on it. We demonstrate that simple, carefully designed systems can obtain high, state-of-the-art accuracies of 73.6% and 76.6% on CNN and Daily Mail

respectively. We do a careful hand-analysis of a small subset of the problems to provide data on their difficulty and what kinds of language understanding are needed to be successful and we try to diagnose what is learned by the systems that we have built. We conclude that: (i) this dataset is easier than previously realized, (ii) straightforward, conventional NLP systems can do much better on it than previously suggested, (iii) the distributed representations of deep learning systems are very effective at recognizing paraphrases, (iv) partly because of the nature of the questions, current systems much more have the nature of single-sentence relation extraction systems than larger-discourse-context text understanding systems, (v) the systems that we present here are close to the ceiling of performance for single-sentence and unambiguous cases of this dataset, and (vi) the prospects for getting the final 20% of questions correct appear poor, since most of them involve issues in the data preparation which undermine the chances of answering the question (coreference errors or anonymization of entities making understanding too difficult).

2 The Reading Comprehension Task

The RC datasets introduced in [Hermann et al.2015] are made from articles on the news websites CNN and Daily Mail, utilizing articles and their bullet point summaries.333The datasets are available at https://github.com/deepmind/rc-data. Figure 1 demonstrates an example444The original article can be found at http://www.cnn.com/2015/03/10/entertainment/feat-star-wars-gay-character/.: it consists of a passage , a question and an answer , where the passage is a news article, the question is a cloze-style task, in which one of the article’s bullet points has had one entity replaced by a placeholder, and the answer is this questioned entity. The goal is to infer the missing entity (answer ) from all the possible entities which appear in the passage. A news article is usually associated with a few (e.g., 3–5) bullet points and each of them highlights one aspect of its content.

The text has been run through a Google NLP pipeline. It it tokenized, lowercased, and named entity recognition and coreference resolution have been run. For each coreference chain containing at least one named entity, all items in the chain are replaced by an @entity

marker, for a distinct index . hermann2015teaching argue convincingly that such a strategy is necessary to ensure that systems approach this task by understanding the passage in front of them, rather than by using world knowledge or a language model to answer questions without needing to understand the passage. However, this also gives the task a somewhat artificial character. On the one hand, systems are greatly helped by entity recognition and coreference having already been performed; on the other, they suffer when either of these modules fail, as they do (in Figure 1

, “the character” should probably be coreferent with @entity14; clearer examples of failure appear later on in our data analysis). Moreover, this inability to use world knowledge also makes it much more difficult for a human to do this task – occasionally it is very difficult or impossible for a human to determine the correct answer when presented with an item anonymized in this way.

CNN Daily Mail
# Train 380,298 879,450
# Dev 3,924 64,835
# Test 3,198 53,182
Passage: avg. tokens 761.8 813.1
Passage: avg. sentences 32.3 28.9
Question: avg. tokens 12.5 14.3
Avg. # entities 26.2 26.2
Table 1: Data statistics of the CNN and Daily Mail datasets. The avg. tokens and sentences in the passage, the avg. tokens in the query, and the number of entities are based on statistics from the training set, but they are similar on the development and test sets.

The creation of the datasets benefits from the sheer volume of news articles available online, so they offer a large and realistic testing ground for statistical models. Table 1 provides some statistics on the two datasets: there are 380k and 879k training examples for CNN and Daily Mail respectively. The passages are around 30 sentences and 800 tokens on average, while each question contains around 12–14 tokens.

In the following sections, we seek to more deeply understand the nature of this dataset. We first build some straightforward systems in order to get a better idea of a lower-bound for the performance of current NLP systems. Then we turn to data analysis of a sample of the items to examine their nature and an upper bound on performance.

3 Our Systems

In this section, we describe two systems we implemented – a conventional entity-centric classifier and an end-to-end neural network. While hermann2015teaching do provide several baselines for performance on the RC task, we suspect that their baselines are not that strong. They attempt to use a frame-semantic parser, and we feel that the poor coverage of that parser undermines the results, and is not representative of what a straightforward NLP system – based on standard approaches to factoid question answering and relation extraction developed over the last 15 years – can achieve. Indeed, their frame-semantic model is markedly inferior to another baseline they provide, a heuristic word distance model. At present just two papers are available presenting results on this RC task, both presenting neural network approaches:

[Hermann et al.2015] and [Hill et al.2016]. While the latter is wrapped in the language of end-to-end memory networks, it actually presents a fairly simple window-based neural network classifier running on the CNN data. Its success again raises questions about the true nature and complexity of the RC task provided by this dataset, which we seek to clarify by building a simple attention-based neural net classifier.

Given the (passage, question, answer) triple , and are sequences of tokens for the passage and question sentence, with containing exactly one “@placeholder” token. The goal is to infer the correct entity that the placeholder corresponds to, where is the set of all abstract entity markers. Note that the correct answer entity must appear in the passage .

3.1 Entity-Centric Classifier

We first build a conventional feature-based classifier, aiming to explore what features are effective for this task. This is similar in spirit to [Wang et al.2015], which at present has very competitive performance on the MCTest RC dataset [Richardson et al.2013]

. The setup of this system is to design a feature vector

for each candidate entity , and to learn a weight vector such that the correct answer is expected to rank higher than all other candidate entities:

(1)

We employ the following feature templates:

  1. Whether entity occurs in the passage.

  2. Whether entity occurs in the question.

  3. The frequency of entity in the passage.

  4. The first position of occurence of entity in the passage.

  5. -gram exact match: whether there is an exact match between the text surrounding the placeholder and the text surrounding entity . We have features for all combinations of matching left and/or right one or two words.

  6. Word distance: we align the placeholder with each occurrence of entity , and compute the average minimum distance of each non-stop question word from the entity in the passage.

  7. Sentence co-occurrence: whether entity co-occurs with another entity or verb that appears in the question, in some sentence of the passage.

  8. Dependency parse match: we dependency parse both the question and all the sentences in the passage, and extract an indicator feature of whether and are both found; similar features are constructed for and .

3.2 End-to-end Neural Network

Our neural network system is based on the AttentiveReader model proposed by [Hermann et al.2015]. The framework can be described in the following three steps (see Figure 2):

Figure 2: Our neural network architecture for the reading comprehension task.
Encoding:

First, all the words are mapped to -dimensional vectors via an embedding matrix ; therefore we have : and .

Next we use a shallow bi-directional recurrent neural network (RNN) with hidden size

to encode contextual embeddings of each word in the passage,

and , where . Meanwhile, we use another bi-directional RNN to map the question to an embedding

. We choose to use Gated Recurrent Unit (GRU)

[Cho et al.2014] in our experiments because it performs similarly but is computationally cheaper than LSTM.

Attention:

In this step, the goal is to compare the question embedding and all the contextual embeddings, and select

the pieces of information that are relevant to the question. We compute a probability distribution

depending on the degree of relevance between word (in its context) and the question and then produce an output vector which is a weighted combination of all contextual embeddings :

(2)
(3)

is used in a bilinear term, which allows us to compute a similarity between and more flexibly than with just a dot product.

Prediction:

Using the output vector , the system outputs the most likely answer using:

(4)

Finally, the system adds a softmax function on top of and adopts a negative log-likelihood objective for training.

Differences from [Hermann et al.2015].

Our model basically follows the AttentiveReader. However, to our surprise, our experiments observed nearly 710% improvement over the original AttentiveReader results on CNN and Daily Mail datasets (discussed in Sec. 4). Concretely, our model has the following differences:

  • We use a bilinear term, instead of a

    layer to compute the relevance (attention) between question and contextual embeddings. The effectiveness of the simple bilinear attention function has been shown previously for neural machine translation by

    [Luong et al.2015].

  • After obtaining the weighted contextual embeddings , we use for direct prediction. In contrast, the original model in [Hermann et al.2015] combined and the question embedding via another non-linear layer before making final predictions. We found that we could remove this layer without harming performance. We believe it is sufficient for the model to learn to return the entity to which it maximally gives attention.

  • The original model considers all the words from the vocabulary in making predictions. We think this is unnecessary, and only predict among entities which appear in the passage.

Of these changes, only the first seems important; the other two just aim at keeping the model simple.

Window-based MemN2Ns [Hill et al.2016].

Another recent neural network approach proposed by [Hill et al.2016] is based on a memory network architecture [Weston et al.2015]. We think it is highly similar in spirit. The biggest difference is their way of encoding passages: they demonstrate that it is most effective to only use a 5-word context window when evaluating a candidate entity and they use a positional unigram approach to encode the contextual embeddings: if a window consists of 5 words , then it is encoded as , resulting in separate embedding matrices to learn. They encode the 5-word window surrounding the placeholder in a similar way and all other words in the question text are ignored. In addition, they simply use a dot product to compute the “relevance” between the question and a contextual embedding. This simple model nevertheless works well, showing the extent to which this RC task can be done by very local context matching.

4 Experiments

4.1 Training Details

For training our conventional classifier, we use the implementation of LambdaMART [Wu et al.2010] in the RankLib package.555https://sourceforge.net/p/lemur/wiki/RankLib/.

We use this ranking algorithm since our problem is naturally a ranking problem and forests of boosted decision trees have been very successful lately (as seen, e.g., in many recent Kaggle competitions). We do not use all the features of

LambdaMART since we are only scoring 1/0 loss on the first ranked proposal, rather than using an IR-style metric to score ranked results. We use Stanford’s neural network dependency parser [Chen and Manning2014] to parse all our document and question text, and all other features can be extracted without additional tools.

For training our neural networks, we only keep the most frequent words (including entity and placeholder markers), and map all other words to an ¡unk¿ token. We choose word embedding size , and use the -dimensional pre-trained GloVe word embeddings [Pennington et al.2014]

for initialization. The attention and output parameters are initialized from a uniform distribution between

, and the GRU weights are initialized from a Gaussian distribution

.

We use hidden size for CNN and 256 for Daily Mail

. Optimization is carried out using vanilla stochastic gradient descent (SGD), with a fixed learning rate of

. We sort all the examples by the length of its passage, and randomly sample a mini-batch of size 32 for each update. We also apply dropout with probability

to the embedding layer and gradient clipping when the norm of gradients exceeds

.

Additionally, we think the original indices of entity markers are generated arbitrarily. We attempt to relabel the entity markers based on their first occurrence in the passage and question 666The first occurring entity is relabeled as @entity1, and the second one is relabeled as @entity2, and so on. and find that this step can make training converge faster as well bring slight gains. We report both results (with and without relabeling) for future reference.

All of our models are run on a single GPU (GeForce GTX TITAN X), with roughly a runtime of 3 hours per epoch for

CNN, and 12 hours per epoch for Daily Mail. We run all the models up to epochs and select the model that achieves the best accuracy on the development set.

We run our models 5 times independently with different random seeds and report average performance across the runs. We also report ensemble results which average the prediction probabilities of the 5 models.

4.2 Main Results

Model CNN Daily Mail
Dev Test Dev Test
Frame-semantic model 36.3 40.2 35.5 35.5
Word distance model 50.5 50.9 56.4 55.5
Deep LSTM Reader 55.0 57.0 63.3 62.2
Attentive Reader 61.6 63.0 70.5 69.0
Impatient Reader 61.8 63.8 69.0 68.0
MemNNs (window memory) 58.0 60.6 N/A N/A
MemNNs (window memory + self-sup.) 63.4 66.8 N/A N/A
MemNNs (ensemble) 66.2 69.4 N/A N/A
Ours: Classifier 67.1 67.9 69.1 68.3
Ours: Neural net 72.5 72.7 76.9 76.0
Ours: Neural net (ensemble) 76.2 76.5 79.5 78.7
Ours: Neural net (relabeling) 73.8 73.6 77.6 76.6
Ours: Neural net (relabeling, ensemble) 77.2 77.6 80.2 79.2
Table 2: Accuracy of all models on the CNN and Daily Mail datasets. Results marked are from [Hermann et al.2015] and results marked are from [Hill et al.2016]. Classifier and Neural net denote our entity-centric classifier and neural network systems respectively. The numbers marked with indicate that the results are from ensemble models.

Table 2 presents our main results. The conventional feature-based classifier obtains accuracy on the CNN test set. Not only does this significantly outperform any of the symbolic approaches reported in [Hermann et al.2015], it also outperforms all the neural network systems from their paper and the best single-system result reported so far from [Hill et al.2016]. This suggests that the task might not be as difficult as suggested, and a simple feature set can cover many of the cases. Table 3 presents a feature ablation analysis of our entity-centric classifier on the development portion of the CNN dataset. It shows that -gram match and frequency of entities are the two most important classes of features.

Features Accuracy
Full model 67.1
whether is in the passage 67.1
whether is in the question 67.0
frequency of 63.7
position of 65.9
-gram match 60.5
word distance 65.4
sentence co-occurrence 66.0
dependency parse match 65.6
Table 3: Feature ablation analysis of our entity-centric classifier on the development portion of the CNN dataset. The numbers denote the accuracy after we exclude each feature from the full system, so a low number indicates an important feature.

More dramatically, our single-model neural network surpasses the previous results by a large margin (over 5%). The relabeling process further improves the results by and , pushing up the state-of-the-art accuracies to 73.6% and 76.6% on the two datasets respectively. The ensembles of models consistently bring further gains.

Concurrently with our paper, kadlec2016text and kobayashi2016dynamic also experiment on these two datasets and report competitive results. However, our model not only still outperforms theirs, but also appears to be structurally simpler. All these recent efforts converge to similar numbers, and we believe that they are approaching the ceiling performance of this task, as we will indicate in the next section.

5 Data Analysis

So far, we have good results via either of our systems. In this section, we aim to conduct an in-depth analysis and answer the following questions: (i) Since the dataset was created in an automatic and heuristic way, how many of the questions are trivial to answer, and how many are noisy and not answerable? (ii) What have these models learned? What are the prospects for further improving them? To study this, we randomly sampled 100 examples from the dev portion of the CNN dataset for analysis (see more details in Appendix A).

5.1 Breakdown of the Examples

Category Question Passage
Exact Match it ’s clear @entity0 is leaning toward @placeholder , says an expert who monitors @entity0 …@entity116 , who follows @entity0 ’s operations and propaganda closely , recently told @entity3 , it ’s clear @entity0 is leaning toward @entity60 in terms of doctrine , ideology and an emphasis on holding territory after operations . …
Para-phrase @placeholder says he understands why @entity0 wo n’t play at his tournament …@entity0 called me personally to let me know that he would n’t be playing here at @entity23 , ” @entity3 said on his @entity21 event ’s website . …
Partial clue a tv movie based on @entity2 ’s book @placeholder casts a @entity76 actor as @entity5 …to @entity12 @entity2 professed that his @entity11 is not a religious book . …
Multiple sent. he ’s doing a his - and - her duet all by himself , @entity6 said of @placeholder …we got some groundbreaking performances , here too , tonight , @entity6 said . we got @entity17 , who will be doing some musical performances . he ’s doing a his - and - her duet all by himself . …
Coref. Error rapper @placeholder ” disgusted , ” cancels upcoming show for @entity280 …with hip - hop star @entity246 saying on @entity247 that he was canceling an upcoming show for the @entity249 . …(but @entity249 = @entity280 = SAEs)
Hard pilot error and snow were reasons stated for @placeholder plane crash …a small aircraft carrying @entity5 , @entity6 and @entity7 the @entity12 @entity3 crashed a few miles from @entity9 , near @entity10 , @entity11 . …
Table 4: Some representative examples from each category.

After carefully analyzing these 100 examples, we roughly classify them into the following categories (if an example satisfies more than one category, we classify it into the earliest one):

Exact match

The nearest words around the placeholder are also found in the passage surrounding an entity marker; the answer is self-evident.

Sentence-level paraphrasing

The question text is entailed/rephrased by exactly one sentence in the passage, so the answer can definitely be identified from that sentence.

Partial clue

In many cases, even though we cannot find a complete semantic match between the question text and some sentence, we are still able to infer the answer through partial clues, such as some word/concept overlap.

Multiple sentences

It requires processing multiple sentences to infer the correct answer.

Coreference errors

It is unavoidable that there are many coreference errors in the dataset. This category includes those examples with critical coreference errors for the answer entity or key entities appearing in the question. Basically we treat this category as “not answerable”.

Ambiguous or very hard

This category includes examples for which we think humans are not able to obtain the correct answer (confidently).

No. Category (%)
1 Exact match 13
2 Paraphrasing 41
3 Partial clue 19
4 Multiple sentences 2
5 Coreference errors 8
6 Ambiguous / hard 17
Table 5:

An estimate of the breakdown of the dataset into classes, based on the analysis of our sampled 100 examples from the

CNN dataset.

Table 5 provides our estimate of the percentage for each category, and Table 4 presents one representative example from each category. To our surprise, “coreference errors” and “ambiguous/hard” cases account for of this sample set, based on our manual analysis, and this certainly will be a barrier for training models with an accuracy much above 75% (although, of course, a model can sometimes make a lucky guess). Additionally, only 2 examples require multiple sentences for inference – this is a lower rate than we expected and hermann2015teaching suggest. Therefore, we hypothesize that in most of the “answerable” cases, the goal is to identify the most relevant (single) sentence, and then to infer the answer based upon it.

5.2 Per-category Performance

Now, we further analyze the predictions of our two systems, based on the above categorization.

Category Classifier Neural net
Exact match 13 (100.0%) 13 (100.0%)
Paraphrasing 32 (78.1%) 39 (95.1%)
Partial clue 14 (73.7%) 17 (89.5%)
Multiple sentences 1 (50.0%) 1 (50.0%)
Coreference errors 4 (50.0%) 3 (37.5%)
Ambiguous / hard 2 (11.8%) 1 (5.9%)
All 66 (66.0%) 74 (74.0%)
Table 6: The per-category performance of our two systems.

As seen in Table 6, we have the following observations: (i) The exact-match cases are quite simple and both systems get 100% correct. (ii) For the ambiguous/hard and entity-linking-error cases, meeting our expectations, both of the systems perform poorly. (iii) The two systems mainly differ in paraphrasing cases, and some of the “partial clue” cases. This clearly shows how neural networks are better capable of learning semantic matches involving paraphrasing or lexical variation between the two sentences. (iv) We believe that the neural-net system already achieves near-optimal performance on all the single-sentence and unambiguous cases. There does not seem to be much useful headroom for exploring more sophisticated natural language understanding approaches on this dataset.

6 Related Tasks

We briefly survey other tasks related to reading comprehension.

MCTest [Richardson et al.2013] is an open-domain reading comprehension task, in the form of fictional short stories, accompanied by multiple-choice questions. It was carefully created using crowd sourcing, and aims at a 7-year-old reading comprehension level.

On the one hand, this dataset has a high demand on various reasoning capacities: over of the questions require multiple sentences to answer and also the questions come in assorted categories (what, why, how, whose, which, etc). On the other hand, the full dataset has only 660 paragraphs in total (each paragraph is associated with 4 questions), which renders training statistical models (especially complex ones) very difficult.

Up to now, the best solutions [Sachan et al.2015, Wang et al.2015] are still heavily relying on manually curated syntactic/semantic features, with the aid of additional knowledge (e.g., word embeddings, lexical/paragraph databases).

Children Book Test [Hill et al.2016] was developed in a similar spirit to the CNN/Daily Mail datasets. It takes any consecutive 21 sentences from a children’s book – the first 20 sentences are used as the passage, and the goal is to infer a missing word in the 21st sentence (question and answer). The questions are also categorized by the type of the missing word: named entity, common noun, preposition or verb. According to the first study on this dataset [Hill et al.2016], a language model (an -gram model or a recurrent neural network) with local context is sufficient for predicting verbs or prepositions; however, for named entities or common nouns, it improves performance to scan through the whole paragraph to make predictions. So far, the best published results are reported by window-based memory networks.

bAbI [Weston et al.2016] is a collection of artificial datasets, consisting of 20 different reasoning types. It encourages the development of models with the ability to chain reasoning, induction/deduction, etc., so that they can answer a question like “The football is in the playground” after reading a sequence of sentences “John is in the playground; Bob is in the office; John picked up the football; Bob went to the kitchen.” Various types of memory networks [Sukhbaatar et al.2015, Kumar et al.2016] have been shown effective on these tasks, and lee2016reasoning show that vector space models based on extensive problem analysis can obtain near-perfect accuracies on all the categories. Despite these promising results, this dataset is limited to a small vocabulary (only 100–200 words) and simple language variations, so there is still a huge gap from real-world datasets that we need to fill in.

7 Conclusion

In this paper, we carefully examined the recent CNN/Daily Mail reading comprehension task. Our systems demonstrated state-of-the-art results, but more importantly, we performed a careful analysis of the dataset by hand.

Overall, we think the CNN/Daily Mail datasets are valuable datasets, which provide a promising avenue for training effective statistical models for reading comprehension tasks. Nevertheless, we argue that: (i) this dataset is still quite noisy due to its method of data creation and coreference errors; (ii) current neural networks have almost reached a performance ceiling on this dataset; and (iii) the required reasoning and inference level of this dataset is still quite simple.

As future work, we need to consider how we can utilize these datasets (and the models trained upon them) to help solve more complex RC reasoning tasks (with less annotated data).

Acknowledgments

We thank the anonymous reviewers for their thoughtful feedback. Stanford University gratefully acknowledges the support of the Defense Advanced Research Projects Agency (DARPA) Deep Exploration and Filtering of Text (DEFT) Program under Air Force Research Laboratory (AFRL) contract no. FA8750-13-2-0040. Any opinions, findings, and conclusion or recommendations expressed in this material are those of the authors and do not necessarily reflect the view of the DARPA, AFRL, or the US government.

References

  • [Berant et al.2014] Jonathan Berant, Vivek Srikumar, Pei-Chun Chen, Abby Vander Linden, Brittany Harding, Brad Huang, Peter Clark, and Christopher D. Manning. 2014. Modeling biological processes for reading comprehension. In

    Empirical Methods in Natural Language Processing (EMNLP)

    , pages 1499–1510.
  • [Burges2013] Christopher J.C. Burges. 2013. Towards the machine comprehension of text: An essay. Technical report, Microsoft Research Technical Report MSR-TR-2013-125.
  • [Chen and Manning2014] Danqi Chen and Christopher Manning. 2014. A fast and accurate dependency parser using neural networks. In Empirical Methods in Natural Language Processing (EMNLP), pages 740–750.
  • [Cho et al.2014] Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using rnn encoder–decoder for statistical machine translation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1724–1734.
  • [Hermann et al.2015] Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015. Teaching machines to read and comprehend. In Advances in Neural Information Processing Systems (NIPS), pages 1684–1692.
  • [Hill et al.2016] Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston. 2016. The goldilocks principle: Reading children’s books with explicit memory representations. In International Conference on Learning Representations (ICLR).
  • [Kadlec et al.2016] Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, and Jan Kleindienst. 2016. Text understanding with the attention sum reader network. In Association for Computational Linguistics (ACL).
  • [Kobayashi et al.2016] Sosuke Kobayashi, Ran Tian, Naoaki Okazaki, and Kentaro Inui. 2016.

    Dynamic entity representation with max-pooling improves machine reading.

    In North American Association for Computational Linguistics (NAACL).
  • [Kumar et al.2016] Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, and Richard Socher. 2016. Ask me anything: Dynamic memory networks for natural language processing. In International Conference on Machine Learning (ICML).
  • [Lee et al.2016] Moontae Lee, Xiaodong He, Wen-tau Yih, Jianfeng Gao, Li Deng, and Paul Smolensky. 2016. Reasoning in vector space: An exploratory study of question answering. In International Conference on Learning Representations (ICLR).
  • [Luong et al.2015] Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective approaches to attention-based neural machine translation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1412–1421.
  • [Norvig1978] Peter Norvig. 1978. A Unified Theory of Inference for Text Understanding. Ph.D. thesis, University of California, Berkeley.
  • [Pennington et al.2014] Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543.
  • [Richardson et al.2013] Matthew Richardson, Christopher J.C. Burges, and Erin Renshaw. 2013. MCTest: A challenge dataset for the open-domain machine comprehension of text. In Empirical Methods in Natural Language Processing (EMNLP), pages 193–203.
  • [Sachan et al.2015] Mrinmaya Sachan, Kumar Dubey, Eric Xing, and Matthew Richardson. 2015. Learning answer-entailing structures for machine comprehension. In Association for Computational Linguistics and International Joint Conference on Natural Language Processing (ACL/IJCNLP), pages 239–249.
  • [Sukhbaatar et al.2015] Sainbayar Sukhbaatar, arthur szlam, Jason Weston, and Rob Fergus. 2015. End-to-end memory networks. In Advances in Neural Information Processing Systems (NIPS), pages 2431–2439.
  • [Wang et al.2015] Hai Wang, Mohit Bansal, Kevin Gimpel, and David McAllester. 2015. Machine comprehension with syntax, frames, and semantics. In Association for Computational Linguistics and International Joint Conference on Natural Language Processing (ACL/IJCNLP), pages 700–706.
  • [Weston et al.2015] Jason Weston, Sumit Chopra, and Antoine Bordes. 2015. Memory networks. In International Conference on Learning Representations (ICLR).
  • [Weston et al.2016] Jason Weston, Antoine Bordes, Sumit Chopra, and Tomas Mikolov. 2016. Towards AI-complete question answering: A set of prerequisite toy tasks. In International Conference on Learning Representations (ICLR).
  • [Wu et al.2010] Qiang Wu, Christopher J. Burges, Krysta M. Svore, and Jianfeng Gao. 2010. Adapting boosting for information retrieval measures. Information Retrieval, pages 254–270.

Appendix A Samples and Labeled Categories from the Cnn Dataset

For the analysis in Section 5, we uniformly sampled 100 examples from the development set of the CNN dataset. Table 8 provides a full index list of our samples and Table 7 presents our labeled categories.

Category Sample IDs
Exact match (13) 8, 11, 23, 27, 28, 32, 43, 57, 63, 72, 86, 87, 99
Sentence-level paraphrasing (41) 0, 2, 7, 9, 12, 14, 16, 18, 19, 20, 29, 30, 31, 34, 36, 37, 39, 41, 42, 44, 47, 48, 52, 54, 58, 64, 65, 66, 69, 73, 74, 78, 80, 81, 82, 84, 85, 90, 92, 95, 96
Partial clues (19) 4, 17, 21, 24, 35, 38, 45, 53, 55, 56, 61, 62, 75, 83, 88, 89, 91, 97, 98
Multiple sentences (2) 5, 76
Coreference errors (8) 6, 22, 40, 46, 51, 60, 68, 94
Ambiguous or very hard (17) 1, 3, 10, 13, 15, 25, 26, 33, 49, 50, 59, 67, 70, 71, 77, 79, 93
Table 7: Our labeled categories of the 100 samples.
ID Filename ID Filename
0 ddb1e746f88a22fee654ecde8f018e7586595045.question 1 2bef8ec21b10a3294b1496d9a86f29f0592d2300.question
2 38c702812a874f983e9890c32ba832841a327351.question 3 636857045cf266dd69b67b1e53617bed5253dc33.question
4 417cbffd5e6275b3c42cb88be222a9f6c7d415f1.question 5 ef96409c707a699e4055a1d0684eecdb6e115c16.question
6 b4e157a6a34bf11a03e0b5cd55065c0f39ac8d60.question 7 1d75e7c59978c7c06f3aecaf52bc35b8919eee17.question
8 223c8e3aeddc3f65fee1964df17bb72f89b723e4.question 9 13d33b8c86375b0f5fdc856116e91a7355c6fc5a.question
10 378fd418b8ec18dff406be07ec225e6bf53659f5.question 11 d8253b7f22662911c19ec4468f81b9db29df1746.question
12 80529c792d3a368861b404c1ce4d7ad3c12e552a.question 13 728e7b365e941d814676168c78c9c4f38892a550.question
14 3cf6fb2c0d09927a12add82b4a3f248da740d0de.question 15 04b827f84e60659258e19806afe9f8d10b764db1.question
16 f0abf359d71f7896abd09ff7b3319c70f2ded81e.question 17 b6696e0f2166a75fcefbe4f28d0ad06e420eef23.question
18 881ab3139c34e9d9f29eb11601321a234d096272.question 19 66f5208d62b543ee41accb7a560d63ff40413bac.question
20 f83a70d469fa667f0952959346b496fbf3cdb35c.question 21 1853813a80f83a1661dd3f6695559674c749525e.question
22 02664d5e3af321afbaf4ee351ba1f24643746451.question 23 20417b5efb836530846ddf677d1bd0bbc831643c.question
24 42c25a01801228a863c508f9d9e95399ea5f37a4.question 25 70a3ba822770abcaf64dd131c85ec964d172c312.question
26 b6636e525ad58ffdc9a7c18187fb3412660d2cdd.question 27 6147c9f2b3d1cc6fbc57c2137f0356513f49bf46.question
28 262b855e2f24e1b2e4e0ba01ace81a1f214d729e.question 29 d7211f4d21f40461bb59954e53360eeb4bb6c664.question
30 be813e58ae9387a9fdaf771656c8e1122794e515.question 31 ad39c5217042f36e4c1458e9397b4a588bbf8cf9.question
32 9534c3907f1cd917d24a9e4f2afc5b38b82d9fca.question 33 3fbe4bfb721a6e1aa60502089c46240d5c332c05.question
34 6efa2d6bad587bde65ca22d10eca83cf0176d84f.question 35 436aa25e28d3a026c4fcd658a852b6a24fc6935e.question
36 0c44d6ef109d33543cfbd26c95c9c3f6fe33a995.question 37 8472b859c5a8d18454644d9acdb5edd1db175eb5.question
38 fb4dd20e0f464423b6407fd0d21cc4384905cf26.question 39 a192ddbcecf2b00260ae4c7c3c20df4d5ce47a85.question
40 f7133f844967483519dbf632e2f3fb90c5625a4c.question 41 29b274958eb057e8f1688f02ef8dbc1c6d06c954.question
42 8ea6ad57c1c5eb1950f50ea47231a5b3f32dd639.question 43 1e43f2349b17dac6d1b3143f8c5556e2257be92c.question
44 7f11f0b4f6bb9aaa3bdc74bffaed5c869b26be97.question 45 8e6d8d984e51adb5071aad22680419854185eaea.question
46 57fc2b7ffcfbd1068fbc33b95d5786e2bff24698.question 47 57b773478955811a8077c98840d85af03e1b4f05.question
48 d857700721b5835c3472ba73ef7abfad0c9c499f.question 49 f8eedded53c96e0cb98e2e95623714d2737f29da.question
50 4c488f41622ad48977a60c2283910f15a736417e.question 51 39680fd0bff53f2ca02f632eabbc024d698f979e.question
52 addd9cebe24c96b4a3c8e9a50cd2a57905b6defb.question 53 50317f7a626e23628e4bfd190e987ad5af7d283e.question
54 3f7ac912a75e4ef7a56987bff37440ffa14770c6.question 55 610012ef561027623f4b4e3b8310c1c41dc819cc.question
56 d9c2e9bfc71045be2ecd959676016599e4637ed1.question 57 848c068db210e0b255f83c4f8b01d2d421fb9c94.question
58 f5c2753703b66d26f43bafe7f157803dc96eedbc.question 59 4f76379f1c7b1d4acc5a4c82ced64af6313698dd.question
60 e5bb1c27d07f1591929bf0283075ad1bc1fc0b50.question 61 33b911f9074c80eb18a57f657ad01393582059be.question
62 58c4c046654af52a3cb8f6890411a41c0dd0063b.question 63 7b03f730fda1b247e9f124b692e3298859785ef3.question
64 ece6f4e047856d5a84811a67ac9780d48044e69a.question 65 35565dc6aecc0f1203842ef13aede0a14a8cf075.question
66 ddf3f2b06353fe8a9b50043f926eb3ab318e91b2.question 67 e248e59739c9c013a2b1b7385d881e0f879b341d.question
68 e86d3fa2a74625620bcae0003dfbe13416ee29cf.question 69 176bf03c9c19951a8ae5197505a568454a6d4526.question
70 ee694cb968ae99aea36f910355bf73da417274c0.question 71 7a666f78590edbaf7c4d73c4ea641c545295a513.question
72 91e3cdd46a70d6dfbe917c6241eab907da4b1562.question 73 e54d9bdcb478ecc490608459d3405571979ef3f2.question
74 f3737e4de9864f083d6697293be650e02505768c.question 75 1fc7488755d24696a4ed1aabc0a21b8b9755d8c6.question
76 fb3eadd07b9f1df1f8a7a6b136ad6d06f4981442.question 77 1406bdad74b3f932342718d5d5d0946a906d73e2.question
78 54b6396669bdb2e30715085745d4f98d058269ef.question 79 0a53102673f2bebc36ce74bf71db1b42a0187052.question
80 d5eb4f98551d23810bfeb0e5b8a94037bcf58b0d.question 81 370de4ffe0f2f9691e4bd456ff344a6a337e0edf.question
82 12f32c770c86083ff21b25de7626505c06440018.question 83 9f6b5cff3ce146e21e323a1462c3eff8fca3d4a0.question
84 1c2a14f525fa3802b8da52aebaa9abd2091f9215.question 85 f2416e14d89d40562284ba2d15f7d5cc59c7e602.question
86 adcf5881856bcbaf1ad93d06a3c5431f6a0319ba.question 87 097d34b804c4c052591984d51444c4a97a3c41ac.question
88 773066c39bb3b593f676caf03f7e7370a8cd2a43.question 89 598cf5ff08ea75dcedda31ac1300e49cdf90893a.question
90 b66ebaaefb844f1216fd3d28eb160b08f42cde62.question 91 535a44842decdc23c11bae50d9393b923897187e.question
92 e27ca3104a596171940db8501c4868ed2fbc8cea.question 93 bb07799b4193cffa90792f92a8c14d591754a7f3.question
94 83ff109c6ccd512abdf317220337b98ef551d94a.question 95 5ede07a1e4ac56a0155d852df0f5bb6bde3cb507.question
96 7a2a9a7fbb44b0e51512c61502ce2292170400c1.question 97 9dcdc052682b041cdbf2fadc8e55f1bafc88fe61.question
98 0c2e28b7f373f29f3796d29047556766cc1dd709.question 99 2bdf1696bfd2579bb719402e9a6fa99cb8dbf587.question
Table 8: A full index list of our samples.