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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 81–90 of 555 papers

TitleStatusHype
Revealing Weaknesses of Vietnamese Language Models Through Unanswerable Questions in Machine Reading Comprehension—0
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension—0
LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension—0
Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading ComprehensionCode1
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
Rethinking Label Smoothing on Multi-hop Question AnsweringCode0
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