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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
基於BERT模型之多國語言機器閱讀理解研究(Multilingual Machine Reading Comprehension based on BERT Model)—0
Integrated Triaging for Fast Reading Comprehension—0
Improving Pre-Trained Multilingual Models with Vocabulary Expansion—0
ASGen: Answer-containing Sentence Generation to Pre-Train Question Generator for Scale-up Data in Question Answering—0
KorQuAD1.0: Korean QA Dataset for Machine Reading Comprehension—0
Symmetric Regularization based BERT for Pair-wise Semantic ReasoningCode0
Semantics-aware BERT for Language UnderstandingCode0
Cross-Lingual Machine Reading ComprehensionCode0
Neural Network-based Models with Commonsense Knowledge for Machine Reading Comprehension—0
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning—0
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