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 9511000 of 1760 papers

TitleStatusHype
A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets0
Memory TransformerCode0
New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles0
Exploring the BERT Cross-Lingual Transferability: a Case Study in Reading Comprehension0
Learning from Demonstration with Weakly Supervised Disentanglement0
On the Multi-Property Extraction and Beyond0
Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network0
Knowledge-Aided Open-Domain Question Answering0
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language ProcessingCode1
GMAT: Global Memory Augmentation for TransformersCode0
DeBERTa: Decoding-enhanced BERT with Disentangled AttentionCode2
Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension0
A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension0
BERT Based Multilingual Machine Comprehension in English and HindiCode0
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
Conversational Machine Comprehension: a Literature Review0
Language Models are Few-Shot LearnersCode3
EMT: Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine ReadingCode1
Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models0
Towards Question Format Independent Numerical Reasoning: A Set of Prerequisite Tasks0
Adversarial Training for Commonsense InferenceCode1
Recurrent Chunking Mechanisms for Long-Text Machine Reading ComprehensionCode1
BIOMRC: A Dataset for Biomedical Machine Reading ComprehensionCode0
Machine Reading Comprehension: The Role of Contextualized Language Models and BeyondCode1
Document Modeling with Graph Attention Networks for Multi-grained Machine Reading ComprehensionCode1
A Dataset for Statutory Reasoning in Tax Law Entailment and Question AnsweringCode1
A Self-Training Method for Machine Reading Comprehension with Soft Evidence ExtractionCode1
Commonsense Evidence Generation and Injection in Reading Comprehension0
How Context Affects Language Models' Factual Predictions0
FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive SummarizationCode1
Building A User-Centric and Content-Driven Socialbot0
To Test Machine Comprehension, Start by Defining Comprehension0
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data0
Teaching Machine Comprehension with Compositional ExplanationsCode1
Is Multihop QA in DiRe Condition? Measuring and Reducing Disconnected ReasoningCode0
NeurQuRI: Neural Question Requirement Inspector for Answerability Prediction in Machine Reading Comprehension0
Neural Symbolic Reader: Scalable Integration of Distributed and Symbolic Representations for Reading Comprehension0
SQuAD2-CR: Semi-supervised Annotation for Cause and Rationales for Unanswerability in SQuAD 2.00
ScholarlyRead: A New Dataset for Scientific Article Reading Comprehension0
Developing Dataset of Japanese Slot Filling Quizzes Designed for Evaluation of Machine Reading Comprehension0
Cross-lingual and Cross-domain Evaluation of Machine Reading Comprehension with Squad and CALOR-Quest Corpora0
Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering0
MathAlign: Linking Formula Identifiers to their Contextual Natural Language Descriptions0
Domain Adapted Distant Supervision for Pedagogically Motivated Relation Extraction0
Linguistic Appropriateness and Pedagogic Usefulness of Reading Comprehension Questions0
WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts0
LagunTest: A NLP Based Application to Enhance Reading Comprehension0
Clinical Reading Comprehension: A Thorough Analysis of the emrQA DatasetCode1
TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions0
Evaluating Neural Machine Comprehension Model Robustness to Noisy Inputs and Adversarial Attacks0
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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