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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 441–450 of 555 papers

TitleStatusHype
Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension—0
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior—0
On Making Reading Comprehension More Comprehensive—0
Improving Pre-Trained Multilingual Model with Vocabulary Expansion—0
Relation Module for Non-answerable Prediction on Question Answering—0
Why can't memory networks read effectively?—0
NumNet: Machine Reading Comprehension with Numerical ReasoningCode0
BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on NovelsCode0
AntMan: Sparse Low-Rank Compression to Accelerate RNN inference—0
MMM: Multi-stage Multi-task Learning for Multi-choice Reading ComprehensionCode0
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