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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
An MRC Framework for Semantic Role Labeling—0
Data Augmentation for Biomedical Factoid Question Answering—0
Event Detection via Derangement Reading Comprehension—0
Cooperative Self-training of Machine Reading Comprehension—0
Semantics-Preserved Distortion for Personal Privacy Protection in Information Management—0
OpenQA: Hybrid QA System Relying on Structured Knowledge Base as well as Non-structured Data—0
Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension—0
From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension—0
Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency GraphCode0
EveMRC: A Two-stage Evidence Modeling For Multi-choice Machine Reading Comprehension—0
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