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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 171–180 of 555 papers

TitleStatusHype
DIFM:An effective deep interaction and fusion model for sentence matching—0
View Dialogue in 2D: A Two-stream Model in Time-speaker Perspective for Dialogue Summarization and beyond—0
Aspect-based Sentiment Analysis as Machine Reading Comprehension—0
基于话头话体共享结构信息的机器阅读理解研究(Rearch on Machine reading comprehension based on shared structure information between Naming and Telling)—0
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning.—0
ET5: A Novel End-to-end Framework for Conversational Machine Reading ComprehensionCode0
Robust Domain Adaptation for Machine Reading Comprehension—0
A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension—0
Unsupervised Domain Adaptation on Question-Answering System with Conversation Data—0
Large-scale Multi-granular Concept Extraction Based on Machine Reading ComprehensionCode0
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