Teaching Machines to Read and Comprehend

Abstract

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.

Appears in: Advances in Neural Information Processing Systems 28 (NIPS 2015). 14 pages, 13 figures

References in corpus (4)

• Sequence to Sequence Learning with Neural Networks
• Natural Language Processing (almost) from Scratch
• Recurrent Models of Visual Attention
• DRAW: A Recurrent Neural Network For Image Generation

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• Hone as You Read: A Practical Type of Interactive Summarization
• Understanding Neural Abstractive Summarization Models via Uncertainty
• MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension
• Towards classification parity across cohorts
• Open-Domain Question Answering with Pre-Constructed Question Spaces
• Learning Sentence Embeddings for Coherence Modelling and Beyond
• Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations
• Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution
• Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources
• Implicit Argument Prediction as Reading Comprehension
• Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks
• NCLS: Neural Cross-Lingual Summarization
• A BERT-based Dual Embedding Model for Chinese Idiom Prediction
• Context-Aware Answer Extraction in Question Answering
• A Novel ILP Framework for Summarizing Content with High Lexical Variety
• Argument Linking: A Survey and Forecast
• Picking Apart Story Salads
• Global-aware Beam Search for Neural Abstractive Summarization
• Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries
• Adversarial Semantic Collisions
• Automatic Text Extractive Summarization Based on Graph and Pre-trained Language Model Attention
• Modeling Human Reading with Neural Attention
• FastSeq: Make Sequence Generation Faster
• Towards Solving Multimodal Comprehension
• Controllable Neural Dialogue Summarization with Personal Named Entity Planning
• Towards Controlled Transformation of Sentiment in Sentences
• On Isotropy Calibration of Transformers
• Evaluation of Abstractive Summarisation Models with Machine Translation in Deliberative Processes
• Visual Summary of Value-level Feature Attribution in Prediction Classes with Recurrent Neural Networks
• Feudal Reinforcement Learning by Reading Manuals
• Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds
• Noised Consistency Training for Text Summarization
• ScopeIt: Scoping Task Relevant Sentences in Documents
• CHEER: Rich Model Helps Poor Model via Knowledge Infusion
• Attention-Based Self-Supervised Feature Learning for Security Data
• Two-Headed Monster And Crossed Co-Attention Networks
• Transductive Learning for Abstractive News Summarization
• Harry Potter and the Action Prediction Challenge from Natural Language
• STRASS: A Light and Effective Method for Extractive Summarization Based on Sentence Embeddings
• What Makes it Difficult to Understand a Scientific Literature?
• Relation/Entity-Centric Reading Comprehension
• Spotify at TREC 2020: Genre-Aware Abstractive Podcast Summarization
• Understanding in Artificial Intelligence
• Muppet: Massive Multi-task Representations with Pre-Finetuning
• Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data
• Convex Aggregation for Opinion Summarization
• Corpus-level and Concept-based Explanations for Interpretable Document Classification
• Detecting Cybersecurity Events from Noisy Short Text
• Logical Parsing from Natural Language Based on a Neural Translation Model
• Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance
• Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning
• Topic Modeling and Progression of American Digital News Media During the Onset of the COVID-19 Pandemic
• Learning to Summarize Passages: Mining Passage-Summary Pairs from Wikipedia Revision Histories
• Reasoning-Driven Question-Answering for Natural Language Understanding
• Why can't memory networks read effectively?
• Machine Reading Comprehension using Case-based Reasoning
• Evaluating the Efficacy of Summarization Evaluation across Languages
• Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization
• Towards Annotating and Creating Sub-Sentence Summary Highlights
• Look-up and Adapt: A One-shot Semantic Parser
• Saliency Maps Generation for Automatic Text Summarization
• Towards Personalized and Human-in-the-Loop Document Summarization
• Quiz-Style Question Generation for News Stories
• Data Augmentation for Abstractive Query-Focused Multi-Document Summarization
• TSSuBERT: Tweet Stream Summarization Using BERT
• Game-Based Video-Context Dialogue
• HydraSum: Disentangling Stylistic Features in Text Summarization using Multi-Decoder Models
• Orthogonal Attention: A Cloze-Style Approach to Negation Scope Resolution
• Unsupervised Extractive Summarization by Human Memory Simulation
• Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model
• Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers
• On Training Instance Selection for Few-Shot Neural Text Generation
• A3Net: Adversarial-and-Attention Network for Machine Reading Comprehension
• An Initial Investigation of Non-Native Spoken Question-Answering
• XRJL-HKUST at SemEval-2021 Task 4: WordNet-Enhanced Dual Multi-head Co-Attention for Reading Comprehension of Abstract Meaning
• Enriching and Controlling Global Semantics for Text Summarization
• Can Edge Probing Tasks Reveal Linguistic Knowledge in QA Models?
• Logic, Language, and Calculus
• Dialogue Summarization with Supporting Utterance Flow Modeling and Fact Regularization
• Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline
• Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension
• Effective Character-augmented Word Embedding for Machine Reading Comprehension
• Inference Time Style Control for Summarization
• Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Losses
• Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation
• Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks
• T3-Vis: a visual analytic framework for Training and fine-Tuning Transformers in NLP
• Joint Parsing and Generation for Abstractive Summarization
• News Article Teaser Tweets and How to Generate Them
• Hierarchical Attention Model for Improved Machine Comprehension of Spoken Content
• Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting
• ClueReader: Heterogeneous Graph Attention Network for Multi-hop Machine Reading Comprehension
• Efficient Mind-Map Generation via Sequence-to-Graph and Reinforced Graph Refinement
• Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation
• Keeping Notes: Conditional Natural Language Generation with a Scratchpad Mechanism
• Generating summaries tailored to target characteristics
• Learning Opinion Summarizers by Selecting Informative Reviews
• Query-Focused Scenario Construction
• ARMAN: Pre-training with Semantically Selecting and Reordering of Sentences for Persian Abstractive Summarization
• Multiplex Graph Neural Network for Extractive Text Summarization
• Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning
• An Exploratory Study on Long Dialogue Summarization: What Works and What's Next
• SummerTime: Text Summarization Toolkit for Non-experts
• Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining
• Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline Generation
• RoR: Read-over-Read for Long Document Machine Reading Comprehension
• Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehension
• Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding
• No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension
• Neural Unification for Logic Reasoning over Natural Language
• CodeQA: A Question Answering Dataset for Source Code Comprehension
• MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization
• Resurrecting Submodularity for Neural Text Generation
• Incremental Reading for Question Answering
• Summary Explorer: Visualizing the State of the Art in Text Summarization