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 751800 of 1760 papers

TitleStatusHype
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension0
Understanding Model Robustness to User-generated Noisy TextsCode0
Retrieval-guided Counterfactual Generation for QA0
Transferring Semantic Knowledge Into Language Encoders0
Advances in Multi-turn Dialogue Comprehension: A Survey0
A Framework for Rationale Extraction for Deep QA models0
I Do Not Understand What I Cannot Define: Automatic Question Generation With Pedagogically-Driven Content Selection0
Multi-tasking Dialogue Comprehension with Discourse ParsingCode0
Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing0
A Study on Contextualized Language Modeling for Machine Reading Comprehension0
Analysing the Effect of Masking Length Distribution of MLM: An Evaluation Framework and Case Study on Chinese MRC Datasets0
Interpretable Semantic Role Relation Table for Supporting Facts Recognition of Reading Comprehension0
Logic Pre-Training of Language Models0
FQuAD2.0: French Question Answering and knowing that you know nothing0
More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering0
Can Question Generation Debias Question Answering Models? A Case Study on Question-Context Lexical OverlapCode0
FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning0
Slot Filling for Biomedical Information ExtractionCode0
Machine Reading Comprehension: Generative or Extractive Reader?0
What Makes Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types0
Numerical reasoning in machine reading comprehension tasks: are we there yet?0
Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading ComprehensionCode0
Abstract, Rationale, Stance: A Joint Model for Scientific Claim VerificationCode0
Modular Self-Supervision for Document-Level Relation Extraction0
Extract, Integrate, Compete: Towards Verification Style Reading ComprehensionCode0
RED: A Novel Dataset for Romanian Emotion Detection from Tweets0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering0
Relying on Discourse Analysis to Answer Complex Questions by Neural Machine Reading Comprehension0
Boosting Search Engines with Interactive Agents0
Unsupervised Open-Domain Question Answering0
Analyzing and Mitigating Interference in Neural Architecture Search0
Smoothing Dialogue States for Open Conversational Machine ReadingCode0
Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension ModelsCode0
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading ComprehensionCode0
A New Entity Extraction Method Based on Machine Reading Comprehension0
BERT-based distractor generation for Swedish reading comprehension questions using a small-scale datasetCode0
An Intelligent Recommendation-cum-Reminder System0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering0
Towards a Better Understanding Human Reading Comprehension with Brain SignalsCode0
Incorporating Compositionality and Morphology into End-to-End Models0
DeepBlueAI at SemEval-2021 Task 1: Lexical Complexity Prediction with A Deep Ensemble Approach0
UoR at SemEval-2021 Task 4: Using Pre-trained BERT Token Embeddings for Question Answering of Abstract Meaning0
面向机器阅读理解的高质量藏语数据集构建(Construction of High-quality Tibetan Dataset for Machine Reading Comprehension)0
Multi-Strategy Knowledge Distillation Based Teacher-Student Framework for Machine Reading Comprehension0
Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes0
基于小句复合体的中文机器阅读理解研究(Machine Reading Comprehension Based on Clause Complex)0
Ti-Reader: 基于注意力机制的藏文机器阅读理解端到端网络模型(Ti-Reader: An End-to-End Network Model Based on Attention Mechanisms for Tibetan Machine Reading Comprehension)0
基于篇章结构攻击的阅读理解任务探究(Analysis of Reading Comprehension Tasks based on passage structure attacks)0
A Chinese Machine Reading Comprehension Dataset Automatic Generated Based on Knowledge Graph0
Towards a more Robust Evaluation for Conversational Question Answering0
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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