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 101150 of 1760 papers

TitleStatusHype
The neural architecture of language: Integrative modeling converges on predictive processingCode1
Introspective Distillation for Robust Question AnsweringCode1
Towards artificial general intelligence via a multimodal foundation modelCode1
On the Robustness of Reading Comprehension Models to Entity RenamingCode1
Tracing Origins: Coreference-aware Machine Reading ComprehensionCode1
Structural Characterization for Dialogue DisentanglementCode1
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional AnswersCode1
Relation-aware Video Reading Comprehension for Temporal Language GroundingCode1
MoEfication: Transformer Feed-forward Layers are Mixtures of ExpertsCode1
EntQA: Entity Linking as Question AnsweringCode1
Single-dataset Experts for Multi-dataset Question AnsweringCode1
MultiDoc2Dial: Modeling Dialogues Grounded in Multiple DocumentsCode1
CodeQA: A Question Answering Dataset for Source Code ComprehensionCode1
Context-NER : Contextual Phrase Generation at ScaleCode1
An MRC Framework for Semantic Role LabelingCode1
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
Interactive Machine Comprehension with Dynamic Knowledge GraphsCode1
How Optimal is Greedy Decoding for Extractive Question Answering?Code1
From LSAT: The Progress and Challenges of Complex ReasoningCode1
Learning Event Graph Knowledge for Abductive ReasoningCode1
Benchmarking: Past, Present and FutureCode1
Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question DecompositionCode1
Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning SkillsCode1
FewCLUE: A Chinese Few-shot Learning Evaluation BenchmarkCode1
Keep it Simple: Unsupervised Simplification of Multi-Paragraph TextCode1
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language UnderstandingCode1
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin InformationCode1
Knowing More About Questions Can Help: Improving Calibration in Question AnsweringCode1
Why Machine Reading Comprehension Models Learn Shortcuts?Code1
SemEval-2021 Task 4: Reading Comprehension of Abstract MeaningCode1
Fact-driven Logical Reasoning for Machine Reading ComprehensionCode1
KLUE: Korean Language Understanding EvaluationCode1
Dependency Parsing as MRC-based Span-Span PredictionCode1
Predicting Text Readability from Scrolling InteractionsCode1
ExpMRC: Explainability Evaluation for Machine Reading ComprehensionCode1
Lawformer: A Pre-trained Language Model for Chinese Legal Long DocumentsCode1
Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of TextCode1
PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel ComputationCode1
ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation ReasoningCode1
NT5?! Training T5 to Perform Numerical ReasoningCode1
Connecting Attributions and QA Model Behavior on Realistic CounterfactualsCode1
Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet ExtractionCode1
Cooperative Self-training of Machine Reading ComprehensionCode1
AnswerQuest: A System for Generating Question-Answer Items from Multi-Paragraph DocumentsCode1
ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract MeaningCode1
Open-Retrieval Conversational Machine ReadingCode1
VisualMRC: Machine Reading Comprehension on Document ImagesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6Unverified
2AMR-LE-EnsembleTest80Unverified
3MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3Unverified
4MERIt(MERIt-deberta-v2-xxlarge )Test79.3Unverified
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