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 351375 of 1760 papers

TitleStatusHype
Asking Again and Again: Exploring LLM Robustness to Repeated QuestionsCode0
Event-Centric Question Answering via Contrastive Learning and Invertible Event TransformationCode0
A Multiple Choices Reading Comprehension Corpus for Vietnamese Language EducationCode0
Chunk, Align, Select: A Simple Long-sequence Processing Method for TransformersCode0
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question AnsweringCode0
Evaluating Large Language Models on Controlled Generation TasksCode0
Fast Reading Comprehension with ConvNetsCode0
FAT ALBERT: Finding Answers in Large Texts using Semantic Similarity Attention Layer based on BERTCode0
Evaluating LLMs for Targeted Concept Simplification for Domain-Specific TextsCode0
Evidence Sentence Extraction for Machine Reading ComprehensionCode0
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited QuestionsCode0
Fine-Grained Prediction of Reading Comprehension from Eye MovementsCode0
ChID: A Large-scale Chinese IDiom Dataset for Cloze TestCode0
Estimating Linguistic Complexity for Science TextsCode0
Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine ReadingCode0
From Dataset Recycling to Multi-Property Extraction and BeyondCode0
ET5: A Novel End-to-end Framework for Conversational Machine Reading ComprehensionCode0
Gated-Attention Readers for Text ComprehensionCode0
Comment Staytime Prediction with LLM-enhanced Comment UnderstandingCode0
Episodic Memory Reader: Learning What to Remember for Question Answering from Streaming DataCode0
EQuANt (Enhanced Question Answer Network)Code0
GENIE: Toward Reproducible and Standardized Human Evaluation for Text GenerationCode0
GMAT: Global Memory Augmentation for TransformersCode0
Entity-Relation Extraction as Multi-Turn Question AnsweringCode0
Entity Tracking Improves Cloze-style Reading ComprehensionCode0
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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