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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 71–80 of 555 papers

TitleStatusHype
Adaptive loose optimization for robust question answeringCode0
NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension—0
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning QuestionCode0
NorQuAD: Norwegian Question Answering DatasetCode1
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
MiniRBT: A Two-stage Distilled Small Chinese Pre-trained ModelCode2
A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets—0
A Multiple Choices Reading Comprehension Corpus for Vietnamese Language EducationCode0
Context-faithful Prompting for Large Language ModelsCode1
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