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 501525 of 1760 papers

TitleStatusHype
A Parallel-Hierarchical Model for Machine Comprehension on Sparse DataCode0
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question AnsweringCode0
Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading ComprehensionCode0
Evaluating Commonsense in Pre-trained Language ModelsCode0
Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?Code0
An Understanding-Oriented Robust Machine Reading Comprehension ModelCode0
Visuo-Linguistic Question Answering (VLQA) ChallengeCode0
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading ComprehensionCode0
ET5: A Novel End-to-end Framework for Conversational Machine Reading ComprehensionCode0
BERT-based distractor generation for Swedish reading comprehension questions using a small-scale datasetCode0
Evaluating Large Language Models on Controlled Generation TasksCode0
Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming DataCode0
A Causal View of Entity Bias in (Large) Language ModelsCode0
EQuANt (Enhanced Question Answer Network)Code0
Estimating Linguistic Complexity for Science TextsCode0
Evaluating LLMs for Targeted Concept Simplification for Domain-Specific TextsCode0
BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on NovelsCode0
BioRead: A New Dataset for Biomedical Reading ComprehensionCode0
Entity-Relation Extraction as Multi-Turn Question AnsweringCode0
BIOMRC: A Dataset for Biomedical Machine Reading ComprehensionCode0
English Machine Reading Comprehension Datasets: A SurveyCode0
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading ComprehensionCode0
Entity Tracking Improves Cloze-style Reading ComprehensionCode0
Adaptation of Deep Bidirectional Multilingual Transformers for Russian LanguageCode0
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension TaskCode0
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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