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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 331–340 of 555 papers

TitleStatusHype
From Dataset Recycling to Multi-Property Extraction and BeyondCode0
Correcting the Misuse: A Method for the Chinese Idiom Cloze Test—0
Q. Can Knowledge Graphs be used to Answer Boolean Questions? A. It’s complicated!—0
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text—0
Event Extraction as Machine Reading Comprehension—0
Scene Restoring for Narrative Machine Reading Comprehension—0
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation—0
QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications—0
Improved Synthetic Training for Reading Comprehension—0
RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question AnsweringCode1
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