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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 51–60 of 555 papers

TitleStatusHype
VisualMRC: Machine Reading Comprehension on Document ImagesCode1
ComQA:Compositional Question Answering via Hierarchical Graph Neural NetworksCode1
TSQA: Tabular Scenario Based Question AnsweringCode1
ECONET: Effective Continual Pretraining of Language Models for Event Temporal ReasoningCode1
RECONSIDER: Re-Ranking using Span-Focused Cross-Attention for Open Domain Question AnsweringCode1
Biomedical named entity recognition using BERT in the machine reading comprehension frameworkCode1
LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical ReasoningCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
ReCO: A Large Scale Chinese Reading Comprehension Dataset on OpinionCode1
Recurrent Chunking Mechanisms for Long-Text Machine Reading ComprehensionCode1
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