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 751775 of 1760 papers

TitleStatusHype
ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation ReasoningCode1
Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language ModelsCode0
Towards Robust Neural Retrieval Models with Synthetic Pre-Training0
Hierarchical Learning for Generation with Long Source Sequences0
NT5?! Training T5 to Perform Numerical ReasoningCode1
Connecting Attributions and QA Model Behavior on Realistic CounterfactualsCode1
Discrete Reasoning Templates for Natural Language Understanding0
ReCAM@IITK at SemEval-2021 Task 4: BERT and ALBERT based Ensemble for Abstract Word PredictionCode0
What does BERT Learn from Arabic Machine Reading Comprehension Datasets?0
Virtual Pre-Service Teacher Assessment and Feedback via Conversational Agents0
Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension?Code0
Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents0
Evaluating Neural Model Robustness for Machine Comprehension0
XRJL-HKUST at SemEval-2021 Task 4: WordNet-Enhanced Dual Multi-head Co-Attention for Reading Comprehension of Abstract MeaningCode0
Incorporating Connections Beyond Knowledge Embeddings: A Plug-and-Play Module to Enhance Commonsense Reasoning in Machine Reading Comprehension0
Complex Factoid Question Answering with a Free-Text Knowledge Graph0
Self-Supervised Test-Time Learning for Reading Comprehension0
Quinductor: a multilingual data-driven method for generating reading-comprehension questions using Universal DependenciesCode0
Robustly Optimized and Distilled Training for Natural Language Understanding0
Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet ExtractionCode1
Cooperative Self-training of Machine Reading ComprehensionCode1
Conversational Answer Generation and Factuality for Reading Comprehension Question-Answering0
MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension0
AnswerQuest: A System for Generating Question-Answer Items from Multi-Paragraph DocumentsCode1
Advances in Multi-turn Dialogue Comprehension: A Survey0
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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