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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 281–290 of 555 papers

TitleStatusHype
Robustly Optimized and Distilled Training for Natural Language Understanding—0
Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet ExtractionCode1
Cooperative Self-training of Machine Reading ComprehensionCode1
MCR-Net: A Multi-Step Co-Interactive Relation Network for Unanswerable Questions on Machine Reading Comprehension—0
OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach—0
Biomedical Question Answering: A Survey of Approaches and Challenges—0
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data—0
Modeling Context in Answer Sentence Selection Systems on a Latency Budget—0
Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning—0
VisualMRC: Machine Reading Comprehension on Document ImagesCode1
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