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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 151–160 of 555 papers

TitleStatusHype
XLMRQA: Open-Domain Question Answering on Vietnamese Wikipedia-based Textual Knowledge Source—0
The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot TransferCode0
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling—0
Data Augmentation for Biomedical Factoid Question AnsweringCode0
Improving Zero-Shot Event Extraction via Sentence Simplification—0
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading ComprehensionCode1
Clozer: Adaptable Data Augmentation for Cloze-style Reading Comprehension—0
Lite Unified Modeling for Discriminative Reading ComprehensionCode0
VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension—0
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading ComprehensionCode1
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