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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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TitleStatusHype
Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet ExtractionCode1
ExpMRC: Explainability Evaluation for Machine Reading ComprehensionCode1
Biomedical named entity recognition using BERT in the machine reading comprehension frameworkCode1
FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigmCode1
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and AugmentationCode1
IDOL: Indicator-oriented Logic Pre-training for Logical ReasoningCode1
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin InformationCode1
An MRC Framework for Semantic Role LabelingCode1
ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper PagesCode1
End-to-End Chinese Speaker IdentificationCode1
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