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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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TitleStatusHype
Pre-Training with Whole Word Masking for Chinese BERTCode3
MiniRBT: A Two-stage Distilled Small Chinese Pre-trained ModelCode2
CLUE: A Chinese Language Understanding Evaluation BenchmarkCode2
The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language VariantsCode2
A Self-Training Method for Machine Reading Comprehension with Soft Evidence ExtractionCode1
A Sentence Cloze Dataset for Chinese Machine Reading ComprehensionCode1
Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation ExtractionCode1
An MRC Framework for Semantic Role LabelingCode1
ArabicaQA: A Comprehensive Dataset for Arabic Question AnsweringCode1
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading ComprehensionCode1
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