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 401450 of 1760 papers

TitleStatusHype
Instance Regularization for Discriminative Language Model Pre-trainingCode0
How Well Do Multi-hop Reading Comprehension Models Understand Date Information?Code0
SpaceQA: Answering Questions about the Design of Space Missions and Space Craft ConceptsCode0
U3E: Unsupervised and Erasure-based Evidence Extraction for Machine Reading Comprehension0
Modular Approach to Machine Reading Comprehension: Mixture of Task-Aware Experts0
Unsupervised Generation of Long-form Technical Questions from Textbook Metadata using Structured Templates0
Machine Reading, Fast and Slow: When Do Models “Understand” Language?0
DoSEA: A Domain-specific Entity-aware Framework for Cross-Domain Named Entity RecogitionCode0
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning.0
Aspect-based Sentiment Analysis as Machine Reading Comprehension0
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning0
Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at Each Single-Hop?Code0
View Dialogue in 2D: A Two-stream Model in Time-speaker Perspective for Dialogue Summarization and beyond0
基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)0
DIFM:An effective deep interaction and fusion model for sentence matching0
基于话头话体共享结构信息的机器阅读理解研究(Rearch on Machine reading comprehension based on shared structure information between Naming and Telling)0
IdeaReader: A Machine Reading System for Understanding the Idea Flow of Scientific Publications0
On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question AnsweringCode0
Multiple-Choice Question Generation: Towards an Automated Assessment Framework0
Robust Domain Adaptation for Machine Reading Comprehension0
ET5: A Novel End-to-end Framework for Conversational Machine Reading ComprehensionCode0
SF-DST: Few-Shot Self-Feeding Reading Comprehension Dialogue State Tracking with Auxiliary Task0
ScreenQA: Large-Scale Question-Answer Pairs over Mobile App ScreenshotsCode1
A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair ExtractionCode1
Machine Reading, Fast and Slow: When Do Models "Understand" Language?0
CoHS-CQG: Context and History Selection for Conversational Question GenerationCode1
A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension0
Zero-shot Event Causality Identification with Question Answering0
Unsupervised Domain Adaptation on Question-Answering System with Conversation Data0
Syntactic Cross and Reading Effort in English to Japanese Translation0
Why Do Neural Language Models Still Need Commonsense Knowledge to Handle Semantic Variations in Question Answering?Code1
Large-scale Multi-granular Concept Extraction Based on Machine Reading ComprehensionCode0
Trigger-free Event Detection via Derangement Reading Comprehension0
Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension0
Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation0
Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State TrackingCode0
To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning0
Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text0
MRCLens: an MRC Dataset Bias Detection Toolkit0
Can large language models reason about medical questions?Code1
Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained ModelsCode0
ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named EntitiesCode1
JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their DescriptionsCode0
OPERA: Operation-Pivoted Discrete Reasoning over TextCode0
End-to-End Chinese Speaker IdentificationCode1
CL-ReLKT: Cross-lingual Language Knowledge Transfer for Multilingual Retrieval Question AnsweringCode1
A Robustly Optimized BMRC for Aspect Sentiment Triplet ExtractionCode1
MultiSpanQA: A Dataset for Multi-Span Question AnsweringCode1
Understand before Answer: Improve Temporal Reading Comprehension via Precise Question Understanding0
Automatic True/False Question Generation for Educational Purpose0
Show:102550
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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