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 51100 of 1760 papers

TitleStatusHype
ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation ReasoningCode1
Estimating Contamination via Perplexity: Quantifying Memorisation in Language Model EvaluationCode1
Adversarial Training for Commonsense InferenceCode1
Evaluating Models' Local Decision Boundaries via Contrast SetsCode1
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective PartitioningCode1
FastMem: Fast Memorization of Prompt Improves Context Awareness of Large Language ModelsCode1
FewCLUE: A Chinese Few-shot Learning Evaluation BenchmarkCode1
FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigmCode1
From Machine Reading Comprehension to Dialogue State Tracking: Bridging the GapCode1
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language ProcessingCode1
Dialogue Graph Modeling for Conversational Machine ReadingCode1
Context-Aware Answer Extraction in Question AnsweringCode1
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional AnswersCode1
Context-faithful Prompting for Large Language ModelsCode1
Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language ModelsCode1
Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity RecognitionCode1
ComQA:Compositional Question Answering via Hierarchical Graph Neural NetworksCode1
Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional UnderstandingCode1
Differentiable Reasoning on Large Knowledge Bases and Natural LanguageCode1
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin InformationCode1
Biomedical named entity recognition using BERT in the machine reading comprehension frameworkCode1
Break It Down: A Question Understanding BenchmarkCode1
ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper PagesCode1
AllenNLP: A Deep Semantic Natural Language Processing PlatformCode1
Benchmarking Robustness of Machine Reading Comprehension ModelsCode1
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No QuestionsCode1
Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question DecompositionCode1
Can large language models reason about medical questions?Code1
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment AnalysisCode1
CL-ReLKT: Cross-lingual Language Knowledge Transfer for Multilingual Retrieval Question AnsweringCode1
CodeQA: A Question Answering Dataset for Source Code ComprehensionCode1
CoHS-CQG: Context and History Selection for Conversational Question GenerationCode1
A Dataset for Statutory Reasoning in Tax Law Entailment and Question AnsweringCode1
CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about NegationCode1
A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair ExtractionCode1
AC-EVAL: Evaluating Ancient Chinese Language Understanding in Large Language ModelsCode1
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical ReasoningCode1
Cooperative Self-training of Machine Reading ComprehensionCode1
CreoleVal: Multilingual Multitask Benchmarks for CreolesCode1
CTRLsum: Towards Generic Controllable Text SummarizationCode1
Debate Helps Supervise Unreliable ExpertsCode1
A Large Cross-Modal Video Retrieval Dataset with Reading ComprehensionCode1
Analyzing Multi-Task Learning for Abstractive Text SummarizationCode1
Densely Connected Attention Propagation for Reading ComprehensionCode1
An MRC Framework for Semantic Role LabelingCode1
An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuningCode1
Document Modeling with Graph Attention Networks for Multi-grained Machine Reading ComprehensionCode1
DocVQA: A Dataset for VQA on Document ImagesCode1
ELASTIC: Numerical Reasoning with Adaptive Symbolic CompilerCode1
Benchmarking: Past, Present and FutureCode1
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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