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 12011250 of 1760 papers

TitleStatusHype
An Adaption of BIOASQ Question Answering dataset for Machine Reading systems by Manual Annotations of Answer Spans.0
Analyse automatique en cadres s\'emantiques pour l'apprentissage de mod\`eles de compr\'ehension de texte (Semantic Frame Parsing for training Machine Reading Comprehension models)0
Analysing the Effect of Masking Length Distribution of MLM: An Evaluation Framework and Case Study on Chinese MRC Datasets0
Analyzing and Mitigating Interference in Neural Architecture Search0
Analyzing Multiple-Choice Reading and Listening Comprehension Tests0
Analyzing Wrap-Up Effects through an Information-Theoretic Lens0
Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks0
An Analysis of Prerequisite Skills for Reading Comprehension0
An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols0
An Annotated Corpus of Picture Stories Retold by Language Learners0
An Annotation Scheme of A Large-scale Multi-party Dialogues Dataset for Discourse Parsing and Machine Comprehension0
An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text0
An Effective Multi-Stage Approach For Question Answering0
Medical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehension0
An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks0
An End-to-End Dialogue State Tracking System with Machine Reading Comprehension and Wide & Deep Classification0
A Neural Comprehensive Ranker (NCR) for Open-Domain Question Answering0
An evaluation of syntactic simplification rules for people with autism0
A New Entity Extraction Method Based on Machine Reading Comprehension0
A New Semantic Lexicon and Similarity Measure in Bangla0
An Experimental Study of Deep Neural Network Models for Vietnamese Multiple-Choice Reading Comprehension0
An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering0
中英文的文字蘊涵與閱讀測驗的初步探索 (An Exploration of Textual Entailment and Reading Comprehension for Chinese and English) [In Chinese]0
An Initial Investigation of Non-Native Spoken Question-Answering0
An Intelligent Recommendation-cum-Reminder System0
An MRC Framework for Semantic Role Labeling0
An NLP-based Reading Tool for Aiding Non-native English Readers0
Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature0
Annotating Entailment Relations for Shortanswer Questions0
Annotating Story Timelines as Temporal Dependency Structures0
Annotating the MASC Corpus with BabelNet0
Annotation Trees: LDC's customizable, extensible, scalable, annotation infrastructure0
A Novel Word Sense Disambiguation Approach Using WordNet Knowledge Graph0
Answerable or Not: Devising a Dataset for Extending Machine Reading Comprehension0
Answer-focused and Position-aware Neural Question Generation0
Answer Generation through Unified Memories over Multiple Passages0
Answering Chinese Elementary School Social Study Multiple Choice Questions0
Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction0
Answer Span Correction in Machine Reading Comprehension0
Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension0
AntMan: Sparse Low-Rank Compression to Accelerate RNN inference0
A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension0
A Participatory Strategy for AI Ethics in Education and Rehabilitation grounded in the Capability Approach0
Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension0
Apples to Apples: Learning Semantics of Common Entities Through a Novel Comprehension Task0
Applications of BERT Based Sequence Tagging Models on Chinese Medical Text Attributes Extraction0
Approximating Givenness in Content Assessment through Distributional Semantics0
A Practical Toolkit for Multilingual Question and Answer Generation0
Show:102550
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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