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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 141–150 of 555 papers

TitleStatusHype
NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension—0
Adaptive loose optimization for robust question answeringCode0
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning QuestionCode0
Information Extraction from Documents: Question Answering vs Token Classification in real-world setups—0
Evaluating the Robustness of Machine Reading Comprehension Models to Low Resource Entity Renaming—0
A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets—0
A Multiple Choices Reading Comprehension Corpus for Vietnamese Language EducationCode0
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
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