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 551600 of 1760 papers

TitleStatusHype
Rethinking Label Smoothing on Multi-hop Question AnsweringCode0
LegalRelectra: Mixed-domain Language Modeling for Long-range Legal Text Comprehension0
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine ReaderCode0
A Comprehensive Survey on Multi-hop Machine Reading Comprehension Approaches0
A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metrics0
Global memory transformer for processing long documents0
Which Shortcut Solution Do Question Answering Models Prefer to Learn?Code0
Automatically generating question-answer pairs for assessing basic reading comprehension in SwedishCode0
InDEX: Indonesian Idiom and Expression Dataset for Cloze Test0
Feature-augmented Machine Reading Comprehension with Auxiliary Tasks0
World Knowledge in Multiple Choice Reading ComprehensionCode0
Complex Reading Comprehension Through Question Decomposition0
1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position SelectorCode0
Empirical Evaluation of Post-Training Quantization Methods for Language Tasks0
Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension0
IDK-MRC: Unanswerable Questions for Indonesian Machine Reading ComprehensionCode0
Event-Centric Question Answering via Contrastive Learning and Invertible Event TransformationCode0
Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and ModelsCode0
Lexical Generalization Improves with Larger Models and Longer TrainingCode0
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions0
JECC: Commonsense Reasoning Tasks Derived from Interactive FictionsCode0
Towards End-to-End Open Conversational Machine ReadingCode0
Rethinking Annotation: Can Language Learners Contribute?0
Step out of KG: Knowledge Graph Completion via Knowledgeable Retrieval and Reading Comprehension0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking0
How Well Do Multi-hop Reading Comprehension Models Understand Date Information?Code0
Instance Regularization for Discriminative Language Model Pre-trainingCode0
SpaceQA: Answering Questions about the Design of Space Missions and Space Craft ConceptsCode0
U3E: Unsupervised and Erasure-based Evidence Extraction for Machine Reading Comprehension0
Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts0
DIFM:An effective deep interaction and fusion model for sentence matching0
Aspect-based Sentiment Analysis as Machine Reading Comprehension0
基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)0
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning.0
Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at Each Single-Hop?Code0
基于话头话体共享结构信息的机器阅读理解研究(Rearch on Machine reading comprehension based on shared structure information between Naming and Telling)0
Machine Reading, Fast and Slow: When Do Models “Understand” Language?0
Unsupervised Generation of Long-form Technical Questions from Textbook Metadata using Structured Templates0
View Dialogue in 2D: A Two-stream Model in Time-speaker Perspective for Dialogue Summarization and beyond0
DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity RecogitionCode0
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning0
IdeaReader: A Machine Reading System for Understanding the Idea Flow of Scientific Publications0
On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question AnsweringCode0
ET5: A Novel End-to-end Framework for Conversational Machine Reading ComprehensionCode0
Multiple-Choice Question Generation: Towards an Automated Assessment Framework0
Robust Domain Adaptation for Machine Reading Comprehension0
SF-DST: Few-Shot Self-Feeding Reading Comprehension Dialogue State Tracking with Auxiliary Task0
Machine Reading, Fast and Slow: When Do Models "Understand" Language?0
A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension0
Unsupervised Domain Adaptation on Question-Answering System with Conversation Data0
Show:102550
← PrevPage 12 of 36Next →

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