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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 221–230 of 555 papers

TitleStatusHype
Using calibrator to improve robustness in Machine Reading Comprehension—0
FedQAS: Privacy-aware machine reading comprehension with federated learningCode0
A Graph Fusion Approach for Cross-Lingual Machine Reading Comprehension—0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension—0
Event Detection via Derangement Reading Comprehension—0
An MRC Framework for Semantic Role Labeling—0
Data Augmentation for Biomedical Factoid Question Answering—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
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