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
Automatic Question Generation using Relative Pronouns and Adverbs0
RECIPE: Applying Open Domain Question Answering to Privacy Policies0
Exploring Semantic Properties of Sentence Embeddings0
SANTO: A Web-based Annotation Tool for Ontology-driven Slot Filling0
A Spatial Model for Extracting and Visualizing Latent Discourse Structure in Text0
A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension0
Multi-glance Reading Model for Text Understanding0
Jack the Reader -- A Machine Reading FrameworkCode0
Semantically Equivalent Adversarial Rules for Debugging NLP modelsCode0
Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis0
Neural Models for Key Phrase Extraction and Question Generation0
Tackling Adversarial Examples in QA via Answer Sentence Selection0
Subword-augmented Embedding for Cloze Reading ComprehensionCode0
Comparative Analysis of Neural QA models on SQuAD0
A Co-Matching Model for Multi-choice Reading ComprehensionCode0
Adaptations of ROUGE and BLEU to Better Evaluate Machine Reading Comprehension Task0
Learning to Search in Long Documents Using Document StructureCode0
DRCD: a Chinese Machine Reading Comprehension DatasetCode0
Scientific Discovery as Link Prediction in Influence and Citation Graphs0
Measuring Frame Instance Relatedness0
Predicting misreadings from gaze in children with reading difficulties0
A Semantic Role-based Approach to Open-Domain Automatic Question Generation0
CAMB at CWI Shared Task 2018: Complex Word Identification with Ensemble-Based Voting0
MITRE at SemEval-2018 Task 11: Commonsense Reasoning without Commonsense Knowledge0
Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog0
ECNU at SemEval-2018 Task 11: Using Deep Learning Method to Address Machine Comprehension Task0
YNU\_Deep at SemEval-2018 Task 11: An Ensemble of Attention-based BiLSTM Models for Machine Comprehension0
YNU\_AI1799 at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge of Different model ensemble0
SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge0
Complex Word Identification Based on Frequency in a Learner Corpus0
Transition-Based Chinese AMR Parsing0
Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences0
Human Needs Categorization of Affective Events Using Labeled and Unlabeled Data0
CLUF: a Neural Model for Second Language Acquisition Modeling0
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding0
A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset0
YNU Deep at SemEval-2018 Task 12: A BiLSTM Model with Neural Attention for Argument Reasoning Comprehension0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment0
YNU-HPCC at Semeval-2018 Task 11: Using an Attention-based CNN-LSTM for Machine Comprehension using Commonsense Knowledge0
Estimating Linguistic Complexity for Science TextsCode0
Read and Comprehend by Gated-Attention Reader with More Belief0
Jiangnan at SemEval-2018 Task 11: Deep Neural Network with Attention Method for Machine Comprehension Task0
Annotating picture description task responses for content analysisCode0
ELiRF-UPV at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge0
IUCM at SemEval-2018 Task 11: Similar-Topic Texts as a Comprehension Knowledge SourceCode0
Generating Feedback for English Foreign Language Exercises0
Lyb3b at SemEval-2018 Task 11: Machine Comprehension Task using Deep Learning Models0
Dependent Gated Reading for Cloze-Style Question Answering0
Efficient and Robust Question Answering from Minimal Context over DocumentsCode0
Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge0
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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