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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 41–50 of 555 papers

TitleStatusHype
ComQA:Compositional Question Answering via Hierarchical Graph Neural NetworksCode1
Context-faithful Prompting for Large Language ModelsCode1
ECONET: Effective Continual Pretraining of Language Models for Event Temporal ReasoningCode1
Dependency Parsing as MRC-based Span-Span PredictionCode1
DUMA: Reading Comprehension with Transposition ThinkingCode1
End-to-End Chinese Speaker IdentificationCode1
FewCLUE: A Chinese Few-shot Learning Evaluation BenchmarkCode1
An MRC Framework for Semantic Role LabelingCode1
From Machine Reading Comprehension to Dialogue State Tracking: Bridging the GapCode1
ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper PagesCode1
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