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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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TitleStatusHype
基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)—0
DIFM:An effective deep interaction and fusion model for sentence matching—0
ET5: A Novel End-to-end Framework for Conversational Machine Reading ComprehensionCode0
Robust Domain Adaptation for Machine Reading Comprehension—0
A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair ExtractionCode1
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
Trigger-free Event Detection via Derangement Reading Comprehension—0
Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension—0
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