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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
Answer-focused and Position-aware Neural Question Generation—0
Answer Generation through Unified Memories over Multiple Passages—0
Answer Span Correction in Machine Reading Comprehension—0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension—0
Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension—0
AntMan: Sparse Low-Rank Compression to Accelerate RNN inference—0
A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension—0
Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension—0
Applications of BERT Based Sequence Tagging Models on Chinese Medical Text Attributes Extraction—0
ARES: A Reading Comprehension Ensembling Service—0
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