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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
The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language VariantsCode2
MiniRBT: A Two-stage Distilled Small Chinese Pre-trained ModelCode2
CLUE: A Chinese Language Understanding Evaluation BenchmarkCode2
Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity RecognitionCode1
ChroniclingAmericaQA: A Large-scale Question Answering Dataset based on Historical American Newspaper PagesCode1
ArabicaQA: A Comprehensive Dataset for Arabic Question AnsweringCode1
Mirror: A Universal Framework for Various Information Extraction TasksCode1
MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading ComprehensionCode1
IDOL: Indicator-oriented Logic Pre-training for Logical ReasoningCode1
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