Teaching Machines to Read and Comprehend

By Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom. 2015

3,880 citations recorded by the source corpus.

Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents of documents that they have seen, but until now large scale training and test datasets have been missing for this type of evaluation. In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data. This allows us to develop a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

A Deep Reinforced Model for Abstractive Summarization (2017, methodology): “Nallapati et al. (2016) also applied their abstrac-tive summarization model on the CNN/Daily Mail dataset (Hermann et al., 2015), which contains input sequences of up to 800 tokens and multi-sentence summaries of up to 100 tokens.”
Structured Attention Networks (2017, background): “, 2015), question answering (Hermann et al., 2015; Sukhbaatar et al., 2015), and algorithm-learning (Graves et al.”
Layer Normalization (2016, methodology): “In order to compare layer normalization to the recently proposed recurrent batch normalization [Cooijmans et al., 2016], we train an unidirectional attentive reader model on the CNN corpus both introduced by Hermann et al. [2015].”
Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering (2018, methodology): “Among them, CNN/Daily Mail (Hermann et al., 2015) and Children’s Book Test (Hill et al., 2015) are the ﬁrst large-scale datasets for reading comprehension task.”
U-Net: Machine Reading Comprehension with Unanswerable Questions (2018, methodology): “Beneﬁting from the rapid development of deep learning techniques and large-scale benchmarks (Hermann et al. 2015; Hill et al. 2015; Rajpurkar et al. 2016), the end-to-end neural methods have achieved promising results on MRC task (Seo et al. 2016; Huang et al. 2017; Chen et al. 2017; Clark and…”
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension (2018, background): “, 2017), CNN/Daily News (Hermann et al., 2015), WikiReading (Hewlett et al.”
Simple and Effective Multi-Paragraph Reading Comprehension (2017, methodology): “The ﬁrst large scale datasets for training neural reading comprehension models used a Cloze-style task, where systems must predict a held out word from a piece of text (Hermann et al., 2015; Hill et al., 2015).”
Bidirectional Attention Flow for Machine Comprehension (2016, methodology): “We also evaluate our model on the task of cloze-style reading comprehension using the CNN and Daily Mail datasets (Hermann et al., 2015).”
SQuAD: 100,000+ Questions for Machine Comprehension of Text (2016, background): “, 2014) are too small for training modern data-intensive models, while (ii) those that are large (Hermann et al., 2015; Hill et al., 2015) are semi-synthetic and do not share the same characteristics as explicit reading comprehension questions.”
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (2019, methodology): “The CNN/Daily Mail [Hermann et al., 2015] dataset was introduced as a question-answering task but was adapted for text summarization by Nallapati et al.”
NumNet: Machine Reading Comprehension with Numerical Reasoning (2019, neutral): “In recent years, researchers have published a large number of annotated MRC datasets such as CNN/Daily Mail (Hermann et al., 2015), SQuAD (Rajpurkar et al., 2016), RACE (Lai et al., 2017), Trivi-aQA (Joshi et al., 2017) and so on.”
Fine-Tuning Language Models from Human Preferences (2019, methodology): “We also applied our method to two summarization tasks: the CNN/Daily Mail dataset of Hermann et al. (2015) and the TL;DR dataset of Völske et al. (2017).”
Natural Questions: A Benchmark for Question Answering Research (2019, methodology): “A number of Cloze-style tasks have been proposed as a method of evaluating reading comprehension (Hermann et al., 2015; Hill et al., 2015; Paperno et al., 2016; Onishi et al., 2016).”
Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function (2019, methodology): “For the training data, we use Daily Mail news articles released by Hermann et al. (2015).”
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension (2018, methodology): “The CNN/Daily Mail Corpus (Hermann et al., 2015), The Children’s Book Test (CBT) (Hill et al., 2015), and LAM-BADA (Paperno et al., 2016) are closely related to Re Co RD : (1) The CNN/Daily Mail Corpus constructed queries from the bullet points, most of which required limited reasoning ability…”
CoQA: A Conversational Question Answering Challenge (2018, background): “…et al., 2013), literature from Project Gutenberg,5 middle and high school English exams from RACE (Lai et al., 2017), news articles from CNN (Hermann et al., 2015), articles from Wikipedia, Reddit articles from the Writing Prompts dataset (Fan et al., 2018), and science articles from AI2…”