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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 61–70 of 555 papers

TitleStatusHype
Machine Reading Comprehension: The Role of Contextualized Language Models and BeyondCode1
Document Modeling with Graph Attention Networks for Multi-grained Machine Reading ComprehensionCode1
A Self-Training Method for Machine Reading Comprehension with Soft Evidence ExtractionCode1
Teaching Machine Comprehension with Compositional ExplanationsCode1
Clinical Reading Comprehension: A Thorough Analysis of the emrQA DatasetCode1
Benchmarking Robustness of Machine Reading Comprehension ModelsCode1
Logic-Guided Data Augmentation and Regularization for Consistent Question AnsweringCode1
From Machine Reading Comprehension to Dialogue State Tracking: Bridging the GapCode1
Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse StructureCode1
A Sentence Cloze Dataset for Chinese Machine Reading ComprehensionCode1
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