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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
MultiDoc2Dial: Modeling Dialogues Grounded in Multiple DocumentsCode1
CodeQA: A Question Answering Dataset for Source Code ComprehensionCode1
Context-NER : Contextual Phrase Generation at ScaleCode1
An MRC Framework for Semantic Role LabelingCode1
RoR: Read-over-Read for Long Document Machine Reading ComprehensionCode1
KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational GraphsCode1
Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading ComprehensionCode1
Interactive Machine Comprehension with Dynamic Knowledge GraphsCode1
FewCLUE: A Chinese Few-shot Learning Evaluation BenchmarkCode1
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin InformationCode1
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