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 14511500 of 1760 papers

TitleStatusHype
Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension0
Towards Inference-Oriented Reading Comprehension: ParallelQA0
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification0
An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols0
Korean L2 Vocabulary Prediction: Can a Large Annotated Corpus be Used to Train Better Models for Predicting Unknown Words?0
Grounding Gradable Adjectives through Crowdsourcing0
Semi-Supervised Clustering for Short Answer Scoring0
Semi-supervised Training Data Generation for Multilingual Question Answering0
EFLLex: A Graded Lexical Resource for Learners of English as a Foreign Language0
Towards AMR-BR: A SemBank for Brazilian Portuguese Language0
BioRead: A New Dataset for Biomedical Reading ComprehensionCode0
Augmenting Image Question Answering Dataset by Exploiting Image Captions0
Attention for Implicit Discourse Relation Recognition0
Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-based Sentiment AnalysisCode0
Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading0
NE-Table: A Neural key-value table for Named EntitiesCode0
A Deep Learning Approach for Forecasting Air Pollution in South Korea Using LSTM0
DuoRC: Towards Complex Language Understanding with Paraphrased Reading ComprehensionCode0
Phrase-Indexed Question Answering: A New Challenge for Scalable Document ComprehensionCode0
Robust Machine Comprehension Models via Adversarial Training0
Neural Models for Reasoning over Multiple Mentions using Coreference0
What Happened? Leveraging VerbNet to Predict the Effects of Actions in Procedural Text0
Training a Ranking Function for Open-Domain Question Answering0
The Training of Neuromodels for Machine Comprehension of Text. Brain2Text Algorithm0
CliCR: A Dataset of Clinical Case Reports for Machine Reading ComprehensionCode0
Pay More Attention - Neural Architectures for Question-Answering0
Multi-range Reasoning for Machine Comprehension0
The Web as a Knowledge-base for Answering Complex QuestionsCode0
HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension0
MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge0
Automating Reading Comprehension by Generating Question and Answer Pairs0
CAESAR: Context Awareness Enabled Summary-Attentive Reader0
Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine ComprehensionCode0
Medical Exam Question Answering with Large-scale Reading Comprehension0
A Question-Focused Multi-Factor Attention Network for Question AnsweringCode0
Assertion-based QA with Question-Aware Open Information Extraction0
An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text0
Adversarial reading networks for machine comprehension0
EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS0
Bidirectional Attention for SQL GenerationCode0
Attentive Memory Networks: Efficient Machine Reading for Conversational SearchCode0
The NarrativeQA Reading Comprehension ChallengeCode0
Stochastic Answer Networks for Machine Reading ComprehensionCode0
Contextualized Word Representations for Reading ComprehensionCode0
FusionNet: Fusing via Fully-Aware Attention with Application to Machine ComprehensionCode0
Dynamic Fusion Networks for Machine Reading Comprehension0
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question AnsweringCode0
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world ApplicationsCode0
Fast Reading Comprehension with ConvNetsCode0
Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension0
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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