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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 31–40 of 555 papers

TitleStatusHype
Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity RecognitionCode1
ArabicaQA: A Comprehensive Dataset for Arabic Question AnsweringCode1
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading ComprehensionCode1
A Robustly Optimized BMRC for Aspect Sentiment Triplet ExtractionCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair ExtractionCode1
A Unified MRC Framework for Named Entity RecognitionCode1
Benchmarking Robustness of Machine Reading Comprehension ModelsCode1
ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper PagesCode1
A Sentence Cloze Dataset for Chinese Machine Reading ComprehensionCode1
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