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 901950 of 1760 papers

TitleStatusHype
A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis0
Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering0
Uncertainty-Based Adaptive Learning for Reading Comprehension0
Machine Reading Comprehension with Enhanced Linguistic Verifiers0
Learning to Generate Questions by Recovering Answer-containing Sentences0
ChemistryQA: A Complex Question Answering Dataset from Chemistry0
Coreference Reasoning in Machine Reading ComprehensionCode0
Using Natural Language Relations between Answer Choices for Machine ComprehensionCode0
SG-Net: Syntax Guided Transformer for Language Representation0
Negation in Cognitive ReasoningCode0
Undivided Attention: Are Intermediate Layers Necessary for BERT?0
Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension0
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading ComprehensionCode0
Reference Knowledgeable Network for Machine Reading ComprehensionCode0
KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning0
Semantics Altering Modifications for Evaluating Comprehension in Machine ReadingCode0
Looking Beyond Short-Premise Natural Language Inference for Downstream Tasks0
End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training0
A Framework and Dataset for Abstract Art Generation via CalligraphyGAN0
Research on Discourse Parsing: from the Dependency View0
Team Solomon at SemEval-2020 Task 4: Be Reasonable: Exploiting Large-scale Language Models for Commonsense Reasoning0
SQL Generation via Machine Reading ComprehensionCode0
Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension0
Robust Machine Reading Comprehension by Learning Soft labels0
ForceReader: a BERT-based Interactive Machine Reading Comprehension Model with Attention Separation0
FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection0
Graph-Based Knowledge Integration for Question Answering over Dialogue0
Bi-directional CognitiveThinking Network for Machine Reading Comprehension0
A Vietnamese Dataset for Evaluating Machine Reading Comprehension0
Recipe Instruction Semantics Corpus (RISeC): Resolving Semantic Structure and Zero Anaphora in Recipes0
Reading Comprehension as Natural Language Inference:A Semantic Analysis0
Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks0
Automatic Evaluation vs. User Preference in Neural Textual QuestionAnswering over COVID-19 Scientific Literature0
Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension0
Automated Graph Generation at Sentence Level for Reading Comprehension Based on Conceptual Graphs0
Read and Reason with MuSeRC and RuCoS: Datasets for Machine Reading Comprehension for Russian0
Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension0
A Multilingual Reading Comprehension System for more than 100 Languages0
NUT-RC: Noisy User-generated Text-oriented Reading ComprehensionCode0
MRC Examples Answerable by BERT without a Question Are Less Effective in MRC Model Training0
Multi-choice Relational Reasoning for Machine Reading Comprehension0
Using Machine Learning and Natural Language Processing Techniques to Analyze and Support Moderation of Student Book Discussions0
IIRC: A Dataset of Incomplete Information Reading Comprehension Questions0
Unsupervised Explanation Generation for Machine Reading Comprehension0
CalibreNet: Calibration Networks for Multilingual Sequence Labeling0
Synonym Knowledge Enhanced Reader for Chinese Idiom Reading ComprehensionCode0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning0
From Dataset Recycling to Multi-Property Extraction and BeyondCode0
Answer Span Correction in Machine Reading Comprehension0
Correcting the Misuse: A Method for the Chinese Idiom Cloze Test0
Show:102550
← PrevPage 19 of 36Next →

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