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 151–200 of 1760 papers

TitleStatusHype
A Unified MRC Framework for Named Entity RecognitionCode1
Differentiable Reasoning on Large Knowledge Bases and Natural LanguageCode1
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No QuestionsCode1
DocTrack: A Visually-Rich Document Dataset Really Aligned with Human Eye Movement for Machine ReadingCode1
ArabicaQA: A Comprehensive Dataset for Arabic Question AnsweringCode1
AraELECTRA: Pre-Training Text Discriminators for Arabic Language UnderstandingCode1
E3: Entailment-driven Extracting and Editing for Conversational Machine ReadingCode1
ELASTIC: Numerical Reasoning with Adaptive Symbolic CompilerCode1
Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity RecognitionCode1
Estimating Contamination via Perplexity: Quantifying Memorisation in Language Model EvaluationCode1
Evaluating Models' Local Decision Boundaries via Contrast SetsCode1
Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4Code1
A Robustly Optimized BMRC for Aspect Sentiment Triplet ExtractionCode1
Fact-driven Logical Reasoning for Machine Reading ComprehensionCode1
A Self-Training Method for Machine Reading Comprehension with Soft Evidence ExtractionCode1
FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive SummarizationCode1
Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question DecompositionCode1
Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet ExtractionCode1
From Machine Reading Comprehension to Dialogue State Tracking: Bridging the GapCode1
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language ProcessingCode1
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
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading ComprehensionCode1
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and AugmentationCode1
IDOL: Indicator-oriented Logic Pre-training for Logical ReasoningCode1
Improving Visual Commonsense in Language Models via Multiple Image GenerationCode1
Incorporating BERT into Neural Machine TranslationCode1
Inquisitive Question Generation for High Level Text ComprehensionCode1
Interactive Machine Comprehension with Dynamic Knowledge GraphsCode1
Introspective Distillation for Robust Question AnsweringCode1
JaQuAD: Japanese Question Answering Dataset for Machine Reading ComprehensionCode1
Keep it Simple: Unsupervised Simplification of Multi-Paragraph TextCode1
Knowing More About Questions Can Help: Improving Calibration in Question AnsweringCode1
Biomedical named entity recognition using BERT in the machine reading comprehension frameworkCode1
Know What You Don't Know: Unanswerable Questions for SQuADCode1
Can large language models reason about medical questions?Code1
Analyzing Multi-Task Learning for Abstractive Text SummarizationCode1
Benchmarking: Past, Present and FutureCode1
Lawformer: A Pre-trained Language Model for Chinese Legal Long DocumentsCode1
Benchmarking Robustness of Machine Reading Comprehension ModelsCode1
LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test ConstructionCode1
Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical ReasoningCode1
Machine Reading Comprehension: The Role of Contextualized Language Models and BeyondCode1
Making Neural QA as Simple as Possible but not SimplerCode1
Automated Scoring for Reading Comprehension via In-context BERT TuningCode1
Beat the AI: Investigating Adversarial Human Annotation for Reading ComprehensionCode1
A Trigger-Sense Memory Flow Framework for Joint Entity and Relation ExtractionCode1
Mirror: A Universal Framework for Various Information Extraction TasksCode1
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse StructureCode1
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment AnalysisCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6—Unverified
2AMR-LE-EnsembleTest80—Unverified
3MERIt(MERIt-deberta-v2-xxlarge )Test79.3—Unverified
4MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3—Unverified
5Knowledge modelTest79.2—Unverified
6DeBERTa-v2-xxlarge-AMR-LE-ContrapositionTest77.2—Unverified
7LReasoner ensembleTest76.1—Unverified
8ELECTRA and ALBERTTest71—Unverified
9WWZTest69.7—Unverified
10xlnet-large-uncased [extended data]Test69.3—Unverified
#ModelMetricClaimedVerifiedStatus
1ALBERT (Ensemble)Accuracy91.4—Unverified
2Megatron-BERT (ensemble)Accuracy90.9—Unverified
3ALBERTxxlarge+DUMA(ensemble)Accuracy89.8—Unverified
4Megatron-BERTAccuracy89.5—Unverified
5XLNetAccuracy (Middle)88.6—Unverified
6DeBERTalargeAccuracy86.8—Unverified
7B10-10-10Accuracy85.7—Unverified
8RoBERTaAccuracy83.2—Unverified
9Orca 2-13BAccuracy82.87—Unverified
10Orca 2-7BAccuracy80.79—Unverified
#ModelMetricClaimedVerifiedStatus
1Golden TransformerAverage F10.94—Unverified
2MT5 LargeAverage F10.84—Unverified
3ruRoberta-large finetuneAverage F10.83—Unverified
4ruT5-large-finetuneAverage F10.82—Unverified
5Human BenchmarkAverage F10.81—Unverified
6ruT5-base-finetuneAverage F10.77—Unverified
7ruBert-large finetuneAverage F10.76—Unverified
8ruBert-base finetuneAverage F10.74—Unverified
9RuGPT3XL few-shotAverage F10.74—Unverified
10RuGPT3LargeAverage F10.73—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-LargeOverall: F164.4—Unverified
2BERT-LargeOverall: F162.7—Unverified
3BiDAFOverall: F128.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BERTMSE0.05—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT pretrained on MIMIC-IIIAnswer F163.55—Unverified