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 601650 of 1760 papers

TitleStatusHype
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension0
Know your tools well: Better and faster QA with synthetic examples0
On the Robustness of Reading Comprehension Models to Entity RenamingCode1
Tracing Origins: Coreference-aware Machine Reading ComprehensionCode1
Structural Characterization for Dialogue DisentanglementCode1
Understanding Model Robustness to User-generated Noisy TextsCode0
Transferring Semantic Knowledge Into Language Encoders0
Retrieval-guided Counterfactual Generation for QA0
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional AnswersCode1
Relation-aware Video Reading Comprehension for Temporal Language GroundingCode1
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
EntQA: Entity Linking as Question AnsweringCode1
MoEfication: Transformer Feed-forward Layers are Mixtures of ExpertsCode1
A Study on Contextualized Language Modeling for Machine Reading Comprehension0
Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing0
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
Single-dataset Experts for Multi-dataset Question AnsweringCode1
FQuAD2.0: French Question Answering and knowing that you know nothing0
MultiDoc2Dial: Modeling Dialogues Grounded in Multiple DocumentsCode1
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
What Makes Reading Comprehension Questions Difficult? Investigating Variation in Passage Sources and Question Types0
Machine Reading Comprehension: Generative or Extractive Reader?0
Slot Filling for Biomedical Information ExtractionCode0
CodeQA: A Question Answering Dataset for Source Code ComprehensionCode1
Numerical reasoning in machine reading comprehension tasks: are we there yet?0
Context-NER : Contextual Phrase Generation at ScaleCode1
An MRC Framework for Semantic Role LabelingCode1
Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading ComprehensionCode0
Abstract, Rationale, Stance: A Joint Model for Scientific Claim VerificationCode0
Extract, Integrate, Compete: Towards Verification Style Reading ComprehensionCode0
Modular Self-Supervision for Document-Level Relation Extraction0
RoR: Read-over-Read for Long Document Machine Reading ComprehensionCode1
KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational GraphsCode1
Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading ComprehensionCode1
Neural News Recommendation with Collaborative News Encoding and Structural User EncodingCode1
Decoupled Transformer for Scalable Inference in Open-domain Question Answering0
Relying on Discourse Analysis to Answer Complex Questions by Neural Machine Reading Comprehension0
RED: A Novel Dataset for Romanian Emotion Detection from Tweets0
Boosting Search Engines with Interactive Agents0
Interactive Machine Comprehension with Dynamic Knowledge GraphsCode1
Unsupervised Open-Domain Question Answering0
Analyzing and Mitigating Interference in Neural Architecture Search0
Smoothing Dialogue States for Open Conversational Machine ReadingCode0
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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