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
Estimating Contamination via Perplexity: Quantifying Memorisation in Language Model EvaluationCode1
Connecting Attributions and QA Model Behavior on Realistic CounterfactualsCode1
Adversarial Training for Commonsense InferenceCode1
Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4Code1
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective PartitioningCode1
FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive SummarizationCode1
FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigmCode1
Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State TrackingCode1
An MRC Framework for Semantic Role LabelingCode1
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
An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuningCode1
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
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment AnalysisCode1
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
Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet ExtractionCode1
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
Benchmarking: Past, Present and FutureCode1
DocTrack: A Visually-Rich Document Dataset Really Aligned with Human Eye Movement for Machine ReadingCode1
DocVQA: A Dataset for VQA on Document ImagesCode1
DUMA: Reading Comprehension with Transposition ThinkingCode1
EMT: Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine ReadingCode1
Benchmarking Robustness of Machine Reading Comprehension ModelsCode1
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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