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 676700 of 1760 papers

TitleStatusHype
What Makes Reading Comprehension Questions Difficult?Code0
Feeding What You Need by Understanding What You Learned0
Read before Generate! Faithful Long Form Question Answering with Machine Reading0
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension TaskCode0
Deep Understanding based Multi-Document Machine Reading Comprehension0
Using calibrator to improve robustness in Machine Reading Comprehension0
Pretraining without Wordpieces: Learning Over a Vocabulary of Millions of Words0
Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models0
PQuAD: A Persian Question Answering Dataset0
FedQAS: Privacy-aware machine reading comprehension with federated learningCode0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?0
An MRC Framework for Semantic Role Labeling0
XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking0
Data Augmentation for Biomedical Factoid Question Answering0
Cooperative Self-training of Machine Reading Comprehension0
Understand before Answer: Improve Temporal Reading Comprehension via Precise Question Understanding0
CL-ReKD: Cross-lingual Knowledge Distillation for Multilingual Retrieval Question Answering0
Event Detection via Derangement Reading Comprehension0
JEFF - Just Another EFFicient Reading Comprehension Test Generation0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension0
A Graph Fusion Approach for Cross-Lingual Machine Reading Comprehension0
Roof-BERT: Divide Understanding Labour and Join in Work0
ChartText: Linking Text with Charts in Documents0
Multi Document Reading Comprehension0
Semantics-Preserved Distortion for Personal Privacy Protection in Information Management0
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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