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 13511400 of 1760 papers

TitleStatusHype
Multi-choice Relational Reasoning for Machine Reading Comprehension0
Multi Document Reading Comprehension0
Multi-glance Reading Model for Text Understanding0
Multi-grained Evidence Inference for Multi-choice Reading Comprehension0
Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension0
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs0
Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network0
Multi-Row, Multi-Span Distant Supervision For Table+Text Question0
Multilingual Question Answering from Formatted Text applied to Conversational Agents0
Towards Zero-Shot Multilingual Synthetic Question and Answer Generation for Cross-Lingual Reading Comprehension0
Multi-Mention Learning for Reading Comprehension with Neural Cascades0
Multi-Modal Citizen Science: From Disambiguation to Transcription of Classical Literature0
MULTI: Multimodal Understanding Leaderboard with Text and Images0
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification0
Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension0
Multi-Perspective Fusion Network for Commonsense Reading Comprehension0
Multiple Choice Question Corpus Analysis for Distractor Characterization0
Multiple-Choice Question Generation: Towards an Automated Assessment Framework0
Multi-range Reasoning for Machine Comprehension0
Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds0
Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering0
Multi-span Style Extraction for Generative Reading Comprehension0
Multi-Stage Pre-training for Low-Resource Domain Adaptation0
Multi-Strategy Knowledge Distillation Based Teacher-Student Framework for Machine Reading Comprehension0
Multi-style Generative Reading Comprehension0
Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification0
Multi-task Learning with Multi-head Attention for Multi-choice Reading Comprehension0
Multi-turn Dialogue Comprehension from a Topic-aware Perspective0
Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge0
Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension0
FedQAS: Privacy-aware machine reading comprehension with federated learningCode0
Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question AnsweringCode0
Span Selection Pre-training for Question AnsweringCode0
Automatic Generation of Inference Making Questions for Reading Comprehension AssessmentsCode0
AmazonQA: A Review-Based Question Answering TaskCode0
FAT ALBERT: Finding Answers in Large Texts using Semantic Similarity Attention Layer based on BERTCode0
Fast Reading Comprehension with ConvNetsCode0
Fine-Grained Prediction of Reading Comprehension from Eye MovementsCode0
FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine ComprehensionCode0
FlowQA: Grasping Flow in History for Conversational Machine ComprehensionCode0
Unsupervised Domain Adaptation on Reading ComprehensionCode0
Speed Reading: Learning to Read ForBackward via ShuttleCode0
Towards End-to-End Open Conversational Machine ReadingCode0
AllenNLP Interpret: A Framework for Explaining Predictions of NLP ModelsCode0
Do Language Models Learn about Legal Entity Types during Pretraining?Code0
Forget Me Not: Reducing Catastrophic Forgetting for Domain Adaptation in Reading ComprehensionCode0
Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?Code0
Document Modeling with External Attention for Sentence ExtractionCode0
Treatment effects without multicollinearity? Temporal order and the Gram-Schmidt process in causal inferenceCode0
Automatic Generation and Evaluation of Reading Comprehension Test Items with Large Language ModelsCode0
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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