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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 151–160 of 555 papers

TitleStatusHype
Cross-Lingual Question Answering over Knowledge Base as Reading ComprehensionCode0
Natural Response Generation for Chinese Reading ComprehensionCode0
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models—0
KILDST: Effective Knowledge-Integrated Learning for Dialogue State Tracking using Gazetteer and Speaker Information—0
Integrating Semantic Information into Sketchy Reading Module of Retro-Reader for Vietnamese Machine Reading Comprehension—0
Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension—0
Medical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehension—0
Rethinking Label Smoothing on Multi-hop Question AnsweringCode0
From Cloze to Comprehension: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine ReaderCode0
A Comprehensive Survey on Multi-hop Machine Reading Comprehension Approaches—0
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