Syntax Vector Learning Using Correspondence for Natural Language Understanding

Syntax Vector Learning Using Correspondence for Natural Language Understanding

Natural language understanding (NLU) is a core technique for implementing natural user interfaces. In this study, we propose a neural network architecture to learn syntax vector representation by employing the correspondence between texts and syntactic structures. For representing the syntactic stru...

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Bibliographic Details
Main Authors: Hyein Seo, Sangkeun Jung, Taewook Hwang, Hyunji Kim, Yoon-Hyung Roh
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Syntax vector learning
syntax similarity
syntax representation
natural language understanding
transformer
deep learning
Online Access:https://ieeexplore.ieee.org/document/9448015/

Internet

https://ieeexplore.ieee.org/document/9448015/

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