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 426450 of 1760 papers

TitleStatusHype
CoHS-CQG: Context and History Selection for Conversational Question GenerationCode1
A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension0
Zero-shot Event Causality Identification with Question Answering0
Unsupervised Domain Adaptation on Question-Answering System with Conversation Data0
Syntactic Cross and Reading Effort in English to Japanese Translation0
Why Do Neural Language Models Still Need Commonsense Knowledge to Handle Semantic Variations in Question Answering?Code1
Large-scale Multi-granular Concept Extraction Based on Machine Reading ComprehensionCode0
Trigger-free Event Detection via Derangement Reading Comprehension0
Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension0
Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation0
Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State TrackingCode0
To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning0
Composing RNNs and FSTs for Small Data: Recovering Missing Characters in Old Hawaiian Text0
MRCLens: an MRC Dataset Bias Detection Toolkit0
Can large language models reason about medical questions?Code1
Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained ModelsCode0
ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named EntitiesCode1
JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their DescriptionsCode0
OPERA: Operation-Pivoted Discrete Reasoning over TextCode0
End-to-End Chinese Speaker IdentificationCode1
CL-ReLKT: Cross-lingual Language Knowledge Transfer for Multilingual Retrieval Question AnsweringCode1
A Robustly Optimized BMRC for Aspect Sentiment Triplet ExtractionCode1
MultiSpanQA: A Dataset for Multi-Span Question AnsweringCode1
Understand before Answer: Improve Temporal Reading Comprehension via Precise Question Understanding0
Automatic True/False Question Generation for Educational Purpose0
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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