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
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous SpaceCode0
Inquisitive Question Generation for High Level Text ComprehensionCode1
Meta Sequence Learning for Generating Adequate Question-Answer Pairs0
Reading Comprehension as Natural Language Inference: A Semantic Analysis0
基于阅读理解框架的中文事件论元抽取(Chinese Event Argument Extraction using Reading Comprehension Framework)0
多模块联合的阅读理解候选句抽取(Evidence sentence extraction for reading comprehension based on multi-module)0
基于多任务学习的生成式阅读理解(Generative Reading Comprehension via Multi-task Learning)0
面向垂直领域的阅读理解数据增强方法(Method for reading comprehension data enhancement in vertical field)0
ARES: A Reading Comprehension Ensembling Service0
Evaluating NLP Models via Contrast Sets0
A Survey on Explainability in Machine Reading Comprehension0
ISAAQ -- Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention0
Bridging Information-Seeking Human Gaze and Machine Reading Comprehension0
A Vietnamese Dataset for Evaluating Machine Reading Comprehension0
MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension0
What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical ExamsCode2
No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension0
Tradeoffs in Sentence Selection Techniques for Open-Domain Question Answering0
Question Directed Graph Attention Network for Numerical Reasoning over TextCode0
Multi-span Style Extraction for Generative Reading Comprehension0
Composing Answer from Multi-spans for Reading Comprehension0
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge0
Biomedical named entity recognition using BERT in the machine reading comprehension frameworkCode1
Revisiting the Open-Domain Question Answering Pipeline0
Knowledge Efficient Deep Learning for Natural Language Processing0
Relation/Entity-Centric Reading Comprehension0
Continual Domain Adaptation for Machine Reading Comprehension0
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and ResourcesCode0
FAT ALBERT: Finding Answers in Large Texts using Semantic Similarity Attention Layer based on BERTCode0
Applications of BERT Based Sequence Tagging Models on Chinese Medical Text Attributes Extraction0
An Experimental Study of Deep Neural Network Models for Vietnamese Multiple-Choice Reading Comprehension0
Ranking Clarification Questions via Natural Language Inference0
App-Aware Response Synthesis for User Reviews0
AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature0
LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical ReasoningCode1
Reading Comprehension in Czech via Machine Translation and Cross-lingual Transfer0
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
DocVQA: A Dataset for VQA on Document ImagesCode1
DeepMet: A Reading Comprehension Paradigm for Token-level Metaphor Detection0
Developing a How-to Tip Machine Comprehension Dataset and its Evaluation in Machine Comprehension by BERT0
A Frame-based Sentence Representation for Machine Reading Comprehension0
Low-Resource Generation of Multi-hop Reasoning Questions0
Machine Reading of Historical EventsCode0
Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine ReadingCode1
Benefits of Intermediate Annotations in Reading Comprehension0
Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering0
Dynamic Sampling Strategies for Multi-Task Reading Comprehension0
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples0
NLPContributions: An Annotation Scheme for Machine Reading of Scholarly Contributions in Natural Language Processing LiteratureCode0
ReCO: A Large Scale Chinese Reading Comprehension Dataset on OpinionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Rational Reasoner / IDOLTest80.6Unverified
2AMR-LE-EnsembleTest80Unverified
3MERIt-deberta-v2-xxlarge deberta.v2.xxlarge.path.override_True.norm_1.1.0.w2.A100.cp200.s42Test79.3Unverified
4MERIt(MERIt-deberta-v2-xxlarge )Test79.3Unverified
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