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Conference Paper
Preliminary study for Conversational Korean-Vietnam Neural Machine Translation
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- Authors
- Seonhui Kim, Seung Yun, Sang-Hun Kim
- Issue Date
- 2023-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2023, pp.1-5
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we aim to build a conversational Korean-Vietnamese translator. We aim to develop a conversational translator that can be applied to interpreting using conversational data that encompasses features such as illformed sentences, anaphora, omissions, and contextual information commonly employed by real individuals in conversations. To this end, we utilized subtitle data to create a large-scale parallel corpus that reflects the characteristics of conversational data and overcome the problem of lack of data between languages, which is a problem in machine translation. We used the built data as training data for a neural networkbased automatic translation model to create a conversational translator, which improved the BLEU score by 3.67 compared to the initial experiment.
- KSP Keywords
- Automatic translation, BLEU score, Contextual information, Conversational data, Lack of data, Large-scale parallel, Machine Translation(MT), Neural machine translation, Parallel Corpus, Preliminary study, Translation Model