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 14011450 of 1760 papers

TitleStatusHype
DRCD: a Chinese Machine Reading Comprehension DatasetCode0
Estimating Linguistic Complexity for Science TextsCode0
Scientific Discovery as Link Prediction in Influence and Citation Graphs0
Generating Feedback for English Foreign Language Exercises0
A Semantic Role-based Approach to Open-Domain Automatic Question Generation0
Complex Word Identification Based on Frequency in a Learner Corpus0
CLUF: a Neural Model for Second Language Acquisition Modeling0
Annotating picture description task responses for content analysisCode0
Predicting misreadings from gaze in children with reading difficulties0
CAMB at CWI Shared Task 2018: Complex Word Identification with Ensemble-Based Voting0
YNU Deep at SemEval-2018 Task 12: A BiLSTM Model with Neural Attention for Argument Reasoning Comprehension0
IUCM at SemEval-2018 Task 11: Similar-Topic Texts as a Comprehension Knowledge SourceCode0
Jiangnan at SemEval-2018 Task 11: Deep Neural Network with Attention Method for Machine Comprehension Task0
ECNU at SemEval-2018 Task 11: Using Deep Learning Method to Address Machine Comprehension Task0
YNU\_AI1799 at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble0
Lyb3b at SemEval-2018 Task 11: Machine Comprehension Task using Deep Learning Models0
YNU\_Deep at SemEval-2018 Task 11: An Ensemble of Attention-based BiLSTM Models for Machine Comprehension0
YNU-HPCC at Semeval-2018 Task 11: Using an Attention-based CNN-LSTM for Machine Comprehension using Commonsense Knowledge0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment0
Measuring Frame Instance Relatedness0
MITRE at SemEval-2018 Task 11: Commonsense Reasoning without Commonsense Knowledge0
ELiRF-UPV at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge0
SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge0
Human Needs Categorization of Affective Events Using Labeled and Unlabeled Data0
Read and Comprehend by Gated-Attention Reader with More Belief0
Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog0
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding0
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences0
Transition-Based Chinese AMR Parsing0
A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset0
Dependent Gated Reading for Cloze-Style Question Answering0
Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge0
Efficient and Robust Question Answering from Minimal Context over DocumentsCode0
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
Augmenting Image Question Answering Dataset by Exploiting Image Captions0
BioRead: A New Dataset for Biomedical Reading ComprehensionCode0
Semi-supervised Training Data Generation for Multilingual Question Answering0
Semi-Supervised Clustering for Short Answer Scoring0
Grounding Gradable Adjectives through Crowdsourcing0
Towards AMR-BR: A SemBank for Brazilian Portuguese Language0
Korean L2 Vocabulary Prediction: Can a Large Annotated Corpus be Used to Train Better Models for Predicting Unknown Words?0
Attention for Implicit Discourse Relation Recognition0
EFLLex: A Graded Lexical Resource for Learners of English as a Foreign Language0
An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols0
Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-based Sentiment AnalysisCode0
Weaver: Deep Co-Encoding of Questions and Documents for Machine Reading0
QANet: Combining Local Convolution with Global Self-Attention for Reading ComprehensionCode1
NE-Table: A Neural key-value table for Named EntitiesCode0
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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