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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 201–210 of 555 papers

TitleStatusHype
On the Robustness of Reading Comprehension Models to Entity RenamingCode1
Tracing Origins: Coreference-aware Machine Reading ComprehensionCode1
Multi-tasking Dialogue Comprehension with Discourse ParsingCode0
MoEfication: Transformer Feed-forward Layers are Mixtures of ExpertsCode1
A Study on Contextualized Language Modeling for Machine Reading Comprehension—0
Analysing the Effect of Masking Length Distribution of MLM: An Evaluation Framework and Case Study on Chinese MRC Datasets—0
Logic Pre-Training of Language Models—0
Interpretable Semantic Role Relation Table for Supporting Facts Recognition of Reading Comprehension—0
MultiDoc2Dial: Modeling Dialogues Grounded in Multiple DocumentsCode1
More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering—0
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