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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 101–110 of 555 papers

TitleStatusHype
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading ComprehensionCode0
BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on NovelsCode0
BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension TaskCode0
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual TransferCode0
Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language ModelsCode0
EQuANt (Enhanced Question Answer Network)Code0
Explaining Interactions Between Text SpansCode0
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading ComprehensionCode0
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine ComprehensionCode0
Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension?Code0
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