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 701750 of 1760 papers

TitleStatusHype
Focus Annotation of Task-based Data: Establishing the Quality of Crowd Annotation0
Focus Annotation of Task-based Data: A Comparison of Expert and Crowd-Sourced Annotation in a Reading Comprehension Corpus0
ForceReader: a BERT-based Interactive Machine Reading Comprehension Model with Attention Separation0
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data0
Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension0
FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection0
Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation0
FQuAD2.0: French Question Answering and Learning When You Don’t Know0
FQuAD: French Question Answering Dataset0
FriendsQA: Open-Domain Question Answering on TV Show Transcripts0
A Spreading Activation Framework for Tracking Conceptual Complexity of Texts0
An Adaption of BIOASQ Question Answering dataset for Machine Reading systems by Manual Annotations of Answer Spans.0
Coarse-grained decomposition and fine-grained interaction for multi-hop question answering0
From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension0
From Light to Rich ERE: Annotation of Entities, Relations, and Events0
End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training0
A Participatory Strategy for AI Ethics in Education and Rehabilitation grounded in the Capability Approach0
Frustratingly Poor Performance of Reading Comprehension Models on Non-adversarial Examples0
Adapting Large Language Models to Domains via Reading Comprehension0
Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension0
End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension0
GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions0
Assessing Chinese Readability using Term Frequency and Lexical Chain0
Analyse automatique en cadres s\'emantiques pour l'apprentissage de mod\`eles de compr\'ehension de texte (Semantic Frame Parsing for training Machine Reading Comprehension models)0
Gated Self-Matching Networks for Reading Comprehension and Question Answering0
Gaze-Driven Sentence Simplification for Language Learners: Enhancing Comprehension and Readability0
Bridging Information-Seeking Human Gaze and Machine Reading Comprehension0
Generalizing Question Answering System with Pre-trained Language Model Fine-tuning0
Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution0
Generating Diagnostic Multiple Choice Comprehension Cloze Questions0
Accurate Supervised and Semi-Supervised Machine Reading for Long Documents0
Generating Feedback for English Foreign Language Exercises0
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning0
Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts0
emrQA-msquad: A Medical Dataset Structured with the SQuAD V2.0 Framework, Enriched with emrQA Medical Information0
Generating Training Data for Semantic Role Labeling based on Label Transfer from Linked Lexical Resources0
Empirical Methods for the Study of Denotation in Nominalizations in Spanish0
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need0
A Strong Lexical Matching Method for the Machine Comprehension Test0
Empirical Evaluation of Post-Training Quantization Methods for Language Tasks0
GenNet : Reading Comprehension with Multiple Choice Questions using Generation and Selection model0
GenQ: Automated Question Generation to Support Caregivers While Reading Stories with Children0
GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval0
Getting the Most out of AMR Parsing0
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension0
Global memory transformer for processing long documents0
Commonsense knowledge adversarial dataset that challenges ELECTRA0
Goal-Oriented Multi-Task BERT-Based Dialogue State Tracker0
GOAT-TTS: Expressive and Realistic Speech Generation via A Dual-Branch LLM0
A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis0
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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