Korean-Vietnamese Neural Machine Translation with Named Entity Recognition and Part-of-Speech Tags
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- VU Van-Hai
- University of Ulsan
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- NGUYEN Quang-Phuoc
- University of Ulsan
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- NGUYEN Kiem-Hieu
- Hanoi University of Science and Technology
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- SHIN Joon-Choul
- University of Ulsan
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- OCK Cheol-Young
- University of Ulsan
抄録
<p>Since deep learning was introduced, a series of achievements has been published in the field of automatic machine translation (MT). However, Korean-Vietnamese MT systems face many challenges because of a lack of data, multiple meanings of individual words, and grammatical diversity that depends on context. Therefore, the quality of Korean-Vietnamese MT systems is still sub-optimal. This paper discusses a method for applying Named Entity Recognition (NER) and Part-of-Speech (POS) tagging to Vietnamese sentences to improve the performance of Korean-Vietnamese MT systems. In terms of implementation, we used a tool to tag NER and POS in Vietnamese sentences. In addition, we had access to a Korean-Vietnamese parallel corpus with more than 450K paired sentences from our previous research paper. The experimental results indicate that tagging NER and POS in Vietnamese sentences can improve the quality of Korean-Vietnamese Neural MT (NMT) in terms of the Bi-Lingual Evaluation Understudy (BLEU) and Translation Error Rate (TER) score. On average, our MT system improved by 1.21 BLEU points or 2.33 TER scores after applying both NER and POS tagging to the Vietnamese corpus. Due to the structural features of language, the MT systems in the Korean to Vietnamese direction always give better BLEU and TER results than translation machines in the reverse direction.</p>
収録刊行物
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- IEICE Transactions on Information and Systems
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IEICE Transactions on Information and Systems E103.D (4), 866-873, 2020-04-01
一般社団法人 電子情報通信学会
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詳細情報 詳細情報について
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- CRID
- 1390846609821311872
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- NII論文ID
- 130007824985
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- ISSN
- 17451361
- 09168532
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可