Developing Language-Specific Models Using a Neural Architecture Search

Applied Sciences

Kang-moon Park¹  YongSuk Yoo² 

[1] Department of Elctronic Engineering, Korea National University of Transportation, Chungju 27469, Chungcheongbuk-do, Korea;Department of English Literature, College of Humanities, Jeonbuk National University, Jeonju-si 54896, Korea;

关键词: deep learning;    neural architecture search;    word ordering;    Korean syntax;   

DOI  :  10.3390/app112110324

来源: DOAJ

【 摘 要 】

This paper applies the neural architecture search (NAS) method to Korean and English grammaticality judgment tasks. Based on the previous research, which only discusses the application of NAS on a Korean dataset, we extend the method to English grammatical tasks and compare the resulting two architectures from Korean and English. Since complex syntactic operations exist beneath the word order that is computed, the two different resulting architectures out of the automated NAS language modeling provide an interesting testbed for future research. To the extent of our knowledge, the methodology adopted here has not been tested in the literature. Crucially, the resulting structure of the NAS application shows an unexpected design for human experts. Furthermore, NAS has generated different models for Korean and English, which have different syntactic operations.

【 授权许可】

Unknown