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Evolutionary Neural Architecture Search (NAS) Using Chromosome Non-Disjunction for Korean Grammaticality Tasks

1
Department of Computer Science, College of Natural Science, Republic of Korea Naval Academy, Changwon-si 51704, Korea
2
Department of Mechanical Systems Engineering, Sookmyung Women’s University, Seoul 04310, Korea
3
Department of Foreign Languages, College of Humanities, Republic of Korea Naval Academy, Changwon-si 51704, Korea
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(10), 3457; https://doi.org/10.3390/app10103457
Received: 19 April 2020 / Revised: 12 May 2020 / Accepted: 14 May 2020 / Published: 17 May 2020
In this paper, we apply the neural architecture search (NAS) method to Korean grammaticality judgment tasks. Since the word order of a language is the final result of complex syntactic operations, a successful neural architecture search in linguistic data suggests that NAS can automate language model designing. Although NAS application to language has been suggested in the literature, we add a novel dataset that contains Korean-specific linguistic operations, which adds great complexity in the patterns. The result of the experiment suggests that NAS provides an architecture for the language. Interestingly, NAS has suggested an unprecedented structure that would not be designed manually. Research on the final topology of the architecture is the topic of our future research. View Full-Text
Keywords: deep learning; neural architecture search; word ordering; Korean syntax deep learning; neural architecture search; word ordering; Korean syntax
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Park, K.-M.; Shin, D.; Yoo, Y. Evolutionary Neural Architecture Search (NAS) Using Chromosome Non-Disjunction for Korean Grammaticality Tasks. Appl. Sci. 2020, 10, 3457.

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