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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
ArabicaQA: A Comprehensive Dataset for Arabic Question AnsweringCode1
DUMA: Reading Comprehension with Transposition ThinkingCode1
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and AugmentationCode1
A Self-Training Method for Machine Reading Comprehension with Soft Evidence ExtractionCode1
A Sentence Cloze Dataset for Chinese Machine Reading ComprehensionCode1
A Unified MRC Framework for Named Entity RecognitionCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
Asking Questions the Human Way: Scalable Question-Answer Generation from Text CorpusCode1
Benchmarking Robustness of Machine Reading Comprehension ModelsCode1
Machine Reading Comprehension: The Role of Contextualized Language Models and BeyondCode1
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