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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Dades bibliogràfiques
Autors principals: Hyein Seo, Sangkeun Jung, Taewook Hwang, Hyunji Kim, Yoon-Hyung Roh
Format: Article
Idioma:English
Publicat: IEEE 2021-01-01
Col·lecció:IEEE Access
Matèries:
Syntax vector learning
syntax similarity
syntax representation
natural language understanding
transformer
deep learning
Accés en línia:https://ieeexplore.ieee.org/document/9448015/

Internet

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

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