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 151200 of 1760 papers

TitleStatusHype
A Unified MRC Framework for Named Entity RecognitionCode1
Automated Scoring for Reading Comprehension via In-context BERT TuningCode1
Document Modeling with Graph Attention Networks for Multi-grained Machine Reading ComprehensionCode1
DocVQA: A Dataset for VQA on Document ImagesCode1
ArabicaQA: A Comprehensive Dataset for Arabic Question AnsweringCode1
AraELECTRA: Pre-Training Text Discriminators for Arabic Language UnderstandingCode1
EMT: Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine ReadingCode1
End-to-End Chinese Speaker IdentificationCode1
Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity RecognitionCode1
Evaluating Models' Local Decision Boundaries via Contrast SetsCode1
Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine ReadingCode1
ExpMRC: Explainability Evaluation for Machine Reading ComprehensionCode1
A Robustly Optimized BMRC for Aspect Sentiment Triplet ExtractionCode1
FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive SummarizationCode1
A Self-Training Method for Machine Reading Comprehension with Soft Evidence ExtractionCode1
FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigmCode1
Can large language models reason about medical questions?Code1
A Dataset for Statutory Reasoning in Tax Law Entailment and Question AnsweringCode1
Generating Distractors for Reading Comprehension Questions from Real ExaminationsCode1
ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper PagesCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
Asking Questions the Human Way: Scalable Question-Answer Generation from Text CorpusCode1
Break It Down: A Question Understanding BenchmarkCode1
HAE-RAE Bench: Evaluation of Korean Knowledge in Language ModelsCode1
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading ComprehensionCode1
Incorporating BERT into Neural Machine TranslationCode1
Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement LearningCode1
Interactive Language Learning by Question AnsweringCode1
Jack the Reader - A Machine Reading FrameworkCode1
JaQuAD: Japanese Question Answering Dataset for Machine Reading ComprehensionCode1
KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational GraphsCode1
KETM:A Knowledge-Enhanced Text Matching methodCode1
Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question DecompositionCode1
Know What You Don't Know: Unanswerable Questions for SQuADCode1
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language UnderstandingCode1
Large Language Models Are Not Strong Abstract ReasonersCode1
Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet ExtractionCode1
Lawformer: A Pre-trained Language Model for Chinese Legal Long DocumentsCode1
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading ComprehensionCode1
Learning Event Graph Knowledge for Abductive ReasoningCode1
Beat the AI: Investigating Adversarial Human Annotation for Reading ComprehensionCode1
LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical ReasoningCode1
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment AnalysisCode1
MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement UnderstandingCode1
Biomedical named entity recognition using BERT in the machine reading comprehension frameworkCode1
Benchmarking: Past, Present and FutureCode1
A Trigger-Sense Memory Flow Framework for Joint Entity and Relation ExtractionCode1
MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension MetricsCode1
Benchmarking Robustness of Machine Reading Comprehension ModelsCode1
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No QuestionsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6Unverified
2AMR-LE-EnsembleTest80Unverified
3MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3Unverified
4MERIt(MERIt-deberta-v2-xxlarge )Test79.3Unverified
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