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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 411–420 of 555 papers

TitleStatusHype
Asking Questions the Human Way: Scalable Question-Answer Generation from Text CorpusCode1
Retrospective Reader for Machine Reading ComprehensionCode1
DUMA: Reading Comprehension with Transposition ThinkingCode1
A Study of the Tasks and Models in Machine Reading Comprehension—0
Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension—0
A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts—0
A Survey on Machine Reading Comprehension Systems—0
Dual Multi-head Co-attention for Multi-choice Reading Comprehension—0
ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension—0
CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension—0
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