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 351400 of 1760 papers

TitleStatusHype
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension0
LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension0
Lexical Complexity Prediction: An Overview0
AmQA: Amharic Question Answering Dataset0
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading ComprehensionCode1
Cross-Lingual Question Answering over Knowledge Base as Reading ComprehensionCode0
Natural Response Generation for Chinese Reading ComprehensionCode0
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models0
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and DevelopmentCode2
KILDST: Effective Knowledge-Integrated Learning for Dialogue State Tracking using Gazetteer and Speaker Information0
Multimodal Inverse Cloze Task for Knowledge-based Visual Question AnsweringCode1
MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement UnderstandingCode1
Integrating Semantic Information into Sketchy Reading Module of Retro-Reader for Vietnamese Machine Reading Comprehension0
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models0
When Do Decompositions Help for Machine Reading?0
Medical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehension0
Rethinking Label Smoothing on Multi-hop Question AnsweringCode0
Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension0
Spans, Not Tokens: A Span-Centric Model for Multi-Span Reading ComprehensionCode1
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
TEMPERA: Test-Time Prompting via Reinforcement LearningCode1
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and AugmentationCode1
Feature-augmented Machine Reading Comprehension with Auxiliary Tasks0
World Knowledge in Multiple Choice Reading ComprehensionCode0
Complex Reading Comprehension Through Question Decomposition0
CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about NegationCode1
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
Analyzing Multi-Task Learning for Abstractive Text SummarizationCode1
IDK-MRC: Unanswerable Questions for Indonesian Machine Reading ComprehensionCode0
Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and ModelsCode0
Event-Centric Question Answering via Contrastive Learning and Invertible Event TransformationCode0
Lexical Generalization Improves with Larger Models and Longer TrainingCode0
NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named EntitiesCode1
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions0
JECC: Commonsense Reasoning Tasks Derived from Interactive FictionsCode0
ELASTIC: Numerical Reasoning with Adaptive Symbolic CompilerCode1
Multitask Pre-training of Modular Prompt for Chinese Few-Shot LearningCode1
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
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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