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Improving the Performance of Vietnamese?Korean Neural Machine Translation with Contextual Embedding

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Abstract
With the recent evolution of deep learning, machine translation (MT) models and systems are being steadily improved. However, research on MT in low-resource languages such as Vietnamese and Korean is still very limited. In recent years, a state-of-the-art context-based embedding model introduced by Google, bidirectional encoder representations for transformers (BERT), has begun to appear in the neural MT (NMT) models in different ways to enhance the accuracy of MT systems.
The BERT model for Vietnamese has been developed and significantly improved in natural language processing (NLP) tasks, such as part-of-speech (POS), named-entity recognition, dependency parsing, and natural language inference. Our research experimented with applying the Vietnamese BERT model to provide POS tagging and morphological analysis (MA) for Vietnamese sentences, and applying word-sense disambiguation (WSD) for Korean sentences in our Vietnamese?Korean bilingual corpus. In the Vietnamese?Korean NMT system, with contextual embedding, the BERT model for Vietnamese is concurrently connected to both encoder layers and decoder layers in the NMT model. Experimental results assessed through BLEU, METEOR, and TER metrics show that contextual embedding significantly improves the quality of Vietnamese?Korean NMT.
Author(s)
부 반 하이웬 콴 푸억빅토리야 에비파테이 투니얀옥철영
Issued Date
2021
Type
Article
Keyword
neural machine translationpart-of-speechword-sense disambiguationKorean-Vietnamese machine translationbidirectional encoder representations from transformers
DOI
10.3390/app112311119
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9178
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_504330e2603c43058f5e15a0cf6531b5&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Improving%20the%20Performance%20of%20Vietnamese%3FKorean%20Neural%20Machine%20Translation%20with%20Contextual%20Embedding&offset=0&pcAvailability=true
Publisher
APPLIED SCIENCES-BASEL
Location
스위스
Language
영어
ISSN
2076-3417
Citation Volume
11
Citation Number
23
Citation Start Page
11119
Citation End Page
11119
Appears in Collections:
Engineering > IT Convergence
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