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Machine Reading Comprehension

Machine Reading Comprehension is one of the key problems in Natural Language Understanding, where the task is to read and comprehend a given text passage, and then answer questions based on it.

Source: Making Neural Machine Reading Comprehension Faster

Papers

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Showing 351–375 of 555 papers

TitleStatusHype
Question Directed Graph Attention Network for Numerical Reasoning over TextCode0
Multi-span Style Extraction for Generative Reading Comprehension—0
Composing Answer from Multi-spans for Reading Comprehension—0
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge—0
Biomedical named entity recognition using BERT in the machine reading comprehension frameworkCode1
Revisiting the Open-Domain Question Answering Pipeline—0
Continual Domain Adaptation for Machine Reading Comprehension—0
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and ResourcesCode0
Applications of BERT Based Sequence Tagging Models on Chinese Medical Text Attributes Extraction—0
An Experimental Study of Deep Neural Network Models for Vietnamese Multiple-Choice Reading Comprehension—0
App-Aware Response Synthesis for User Reviews—0
LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical ReasoningCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
Low-Resource Generation of Multi-hop Reasoning Questions—0
Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering—0
A Frame-based Sentence Representation for Machine Reading Comprehension—0
ReCO: A Large Scale Chinese Reading Comprehension Dataset on OpinionCode1
A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets—0
New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles—0
On the Multi-Property Extraction and Beyond—0
Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension—0
A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension—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
Conversational Machine Comprehension: a Literature Review—0
Recurrent Chunking Mechanisms for Long-Text Machine Reading ComprehensionCode1
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