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 16011650 of 1760 papers

TitleStatusHype
Machine Comprehension Using Match-LSTM and Answer PointerCode0
Hierarchical Attention Model for Improved Machine Comprehension of Spoken Content0
Towards Machine Comprehension of Spoken Content: Initial TOEFL Listening Comprehension Test by Machine0
Who did What: A Large-Scale Person-Centered Cloze Dataset0
Focus Annotation of Task-based Data: Establishing the Quality of Crowd Annotation0
Do We Really Need All Those Rich Linguistic Features? A Neural Network-Based Approach to Implicit Sense LabelingCode0
Approximating Givenness in Content Assessment through Distributional Semantics0
Learning to Jointly Predict Ellipsis and Comparison Structures0
Specifying and Annotating Reduced Argument Span Via QA-SRL0
Creating Interactive Macaronic Interfaces for Language Learning0
Controlled and Balanced Dataset for Japanese Lexical SimplificationCode0
Machine Comprehension using Rich Semantic Representations0
Attention-over-Attention Neural Networks for Reading ComprehensionCode0
Separating Answers from Queries for Neural Reading Comprehension0
Consensus Attention-based Neural Networks for Chinese Reading Comprehension0
SQuAD: 100,000+ Questions for Machine Comprehension of TextCode1
Dialog state tracking, a machine reading approach using Memory Network0
A Thorough Examination of the CNN/Daily Mail Reading Comprehension TaskCode0
Natural Language Comprehension with the EpiReader0
Iterative Alternating Neural Attention for Machine ReadingCode0
Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution0
Gated-Attention Readers for Text ComprehensionCode0
Investigating Active Learning for Short-Answer Scoring0
Automatic Generation of Context-Based Fill-in-the-Blank Exercises Using Co-occurrence Likelihoods and Google n-grams0
Exploring the Intersection of Short Answer Assessment, Authorship Attribution, and Plagiarism Detection0
UNIMELB at SemEval-2016 Tasks 4A and 4B: An Ensemble of Neural Networks and a Word2Vec Based Model for Sentiment ClassificationCode0
Amrita\_CEN at SemEval-2016 Task 1: Semantic Relation from Word Embeddings in Higher Dimension0
Knowledge-Guided Linguistic Rewrites for Inference Rule VerificationCode0
Emergent: a novel data-set for stance classification0
Dynamic Entity Representation with Max-pooling Improves Machine Reading0
Inferring Psycholinguistic Properties of Words0
Mapping Verbs in Different Languages to Knowledge Base Relations using Web Text as Interlingua0
Joint Learning of Sentence Embeddings for Relevance and EntailmentCode0
Machine Comprehension Based on Learning to Rank0
A Reading Comprehension Corpus for Machine Translation EvaluationCode0
Focus Annotation of Task-based Data: A Comparison of Expert and Crowd-Sourced Annotation in a Reading Comprehension Corpus0
Extracting Structured Scholarly Information from the Machine Translation Literature0
A Corpus of Text Data and Gaze Fixations from Autistic and Non-Autistic Adults0
Evaluating the Readability of Text Simplification Output for Readers with Cognitive Disabilities0
Improving POS Tagging of German Learner Language in a Reading Comprehension Scenario0
Recurrent Batch NormalizationCode0
A Parallel-Hierarchical Model for Machine Comprehension on Sparse DataCode0
Sentence Pair Scoring: Towards Unified Framework for Text ComprehensionCode0
Text Understanding with the Attention Sum Reader NetworkCode0
Attention-Based Convolutional Neural Network for Machine Comprehension0
Long Short-Term Memory-Networks for Machine ReadingCode0
Generating Training Data for Semantic Role Labeling based on Label Transfer from Linked Lexical Resources0
What Makes it Difficult to Understand a Scientific Literature?0
Designing a Tag-Based Statistical Math Word Problem Solver with Reasoning and Explanation0
Explanation Generation for a Math Word Problem Solver0
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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