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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 21–30 of 555 papers

TitleStatusHype
A Robustly Optimized BMRC for Aspect Sentiment Triplet ExtractionCode1
ArabicaQA: A Comprehensive Dataset for Arabic Question AnsweringCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
Asking Questions the Human Way: Scalable Question-Answer Generation from Text CorpusCode1
Cooperative Self-training of Machine Reading ComprehensionCode1
A Unified MRC Framework for Named Entity RecognitionCode1
Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet ExtractionCode1
Biomedical named entity recognition using BERT in the machine reading comprehension frameworkCode1
Document Modeling with Graph Attention Networks for Multi-grained Machine Reading ComprehensionCode1
An MRC Framework for Semantic Role LabelingCode1
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