SOTAVerified

Reading Comprehension

Most current question answering datasets frame the task as reading comprehension where the question is about a paragraph or document and the answer often is a span in the document.

Some specific tasks of reading comprehension include multi-modal machine reading comprehension and textual machine reading comprehension, among others. In the literature, machine reading comprehension can be divide into four categories: cloze style, multiple choice, span prediction, and free-form answer. Read more about each category here.

Benchmark datasets used for testing a model's reading comprehension abilities include MovieQA, ReCoRD, and RACE, among others.

The Machine Reading group at UCL also provides an overview of reading comprehension tasks.

Figure source: A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets

Papers

Showing 12011250 of 1760 papers

TitleStatusHype
XLNet: Generalized Autoregressive Pretraining for Language UnderstandingCode1
Pre-Training with Whole Word Masking for Chinese BERTCode3
Automatic learner summary assessment for reading comprehension0
Zero-Shot Entity Linking by Reading Entity DescriptionsCode1
Structured Pruning of Recurrent Neural Networks through Neuron Selection0
Learning to Ask Unanswerable Questions for Machine Reading Comprehension0
Augmenting Neural Networks with First-order LogicCode0
E3: Entailment-driven Extracting and Editing for Conversational Machine ReadingCode1
Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading ComprehensionCode0
Neural Arabic Question AnsweringCode0
Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading ComprehensionCode0
A Survey on Neural Machine Reading Comprehension0
RankQA: Neural Question Answering with Answer Re-RankingCode0
Multi-hop Reading Comprehension through Question Decomposition and RescoringCode0
Compositional Questions Do Not Necessitate Multi-hop ReasoningCode0
Conversing by Reading: Contentful Neural Conversation with On-demand Machine ReadingCode0
Generating Question-Answer HierarchiesCode0
ChID: A Large-scale Chinese IDiom Dataset for Cloze TestCode0
Question Answering as an Automatic Evaluation Metric for News Article SummarizationCode0
Understanding the Polarity of Events in the Biomedical Literature: Deep Learning vs. Linguistically-informed Methods0
Eidos, INDRA, \& Delphi: From Free Text to Executable Causal ModelsCode0
Document-Level N-ary Relation Extraction with Multiscale Representation Learning0
Online Distilling from Checkpoints for Neural Machine Translation0
Yimmon at SemEval-2019 Task 9: Suggestion Mining with Hybrid Augmented Approaches0
Is It Dish Washer Safe? Automatically Answering ``Yes/No'' Questions Using Customer Reviews0
Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering0
MultiQA: An Empirical Investigation of Generalization and Transfer in Reading ComprehensionCode0
A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension0
DSReg: Using Distant Supervision as a Regularizer0
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives0
Controlling Risk of Web Question Answering0
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No QuestionsCode1
MCScript2.0: A Machine Comprehension Corpus Focused on Script Events and Participants0
Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document TraversalCode0
Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction0
Adaptation of Deep Bidirectional Multilingual Transformers for Russian LanguageCode0
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs0
Learning Open Information Extraction of Implicit Relations from Reading Comprehension Datasets0
Multi-step Retriever-Reader Interaction for Scalable Open-domain Question AnsweringCode0
Entity-Relation Extraction as Multi-Turn Question AnsweringCode0
Cognitive Graph for Multi-Hop Reading Comprehension at ScaleCode0
The relational processing limits of classic and contemporary neural network models of language processingCode0
Scalable Neural Theorem Proving on Knowledge Bases and Natural Language0
Routing Networks and the Challenges of Modular and Compositional ComputationCode0
Understanding Dataset Design Choices for Multi-hop Reasoning0
Investigating Prior Knowledge for Challenging Chinese Machine Reading ComprehensionCode0
Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation0
ElimiNet: A Model for Eliminating Options for Reading Comprehension with Multiple Choice QuestionsCode0
Frustratingly Poor Performance of Reading Comprehension Models on Non-adversarial Examples0
Document-Level N-ary Relation Extraction with Multiscale Representation Learning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6Unverified
2AMR-LE-EnsembleTest80Unverified
3MERIt(MERIt-deberta-v2-xxlarge )Test79.3Unverified
4MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3Unverified
5Knowledge modelTest79.2Unverified
6DeBERTa-v2-xxlarge-AMR-LE-ContrapositionTest77.2Unverified
7LReasoner ensembleTest76.1Unverified
8ELECTRA and ALBERTTest71Unverified
9WWZTest69.7Unverified
10xlnet-large-uncased [extended data]Test69.3Unverified
#ModelMetricClaimedVerifiedStatus
1ALBERT (Ensemble)Accuracy91.4Unverified
2Megatron-BERT (ensemble)Accuracy90.9Unverified
3ALBERTxxlarge+DUMA(ensemble)Accuracy89.8Unverified
4Megatron-BERTAccuracy89.5Unverified
5XLNetAccuracy (Middle)88.6Unverified
6DeBERTalargeAccuracy86.8Unverified
7B10-10-10Accuracy85.7Unverified
8RoBERTaAccuracy83.2Unverified
9Orca 2-13BAccuracy82.87Unverified
10Orca 2-7BAccuracy80.79Unverified
#ModelMetricClaimedVerifiedStatus
1Golden TransformerAverage F10.94Unverified
2MT5 LargeAverage F10.84Unverified
3ruRoberta-large finetuneAverage F10.83Unverified
4ruT5-large-finetuneAverage F10.82Unverified
5Human BenchmarkAverage F10.81Unverified
6ruT5-base-finetuneAverage F10.77Unverified
7ruBert-large finetuneAverage F10.76Unverified
8ruBert-base finetuneAverage F10.74Unverified
9RuGPT3XL few-shotAverage F10.74Unverified
10RuGPT3LargeAverage F10.73Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-LargeOverall: F164.4Unverified
2BERT-LargeOverall: F162.7Unverified
3BiDAFOverall: F128.5Unverified
#ModelMetricClaimedVerifiedStatus
1BERTMSE0.05Unverified
#ModelMetricClaimedVerifiedStatus
1BERT pretrained on MIMIC-IIIAnswer F163.55Unverified