Abstract
The primary limitation of North Korean to English translation is the lack of a parallel corpus; therefore, high translation accuracy cannot be achieved. To address this problem, we propose a zero-shot approach using South Korean data, which are remarkably similar to North Korean data. We train a neural machine translation model after tokenizing a South Korean text at the character level and decomposing characters into phonemes.We demonstrate that our method can effectively learn North Korean to English translation and improve the BLEU scores by +1.01 points in comparison with the baseline.- Anthology ID:
- 2020.acl-srw.11
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 72–78
- Language:
- URL:
- https://aclanthology.org/2020.acl-srw.11
- DOI:
- 10.18653/v1/2020.acl-srw.11
- Cite (ACL):
- Hwichan Kim, Tosho Hirasawa, and Mamoru Komachi. 2020. Zero-shot North Korean to English Neural Machine Translation by Character Tokenization and Phoneme Decomposition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 72–78, Online. Association for Computational Linguistics.
- Cite (Informal):
- Zero-shot North Korean to English Neural Machine Translation by Character Tokenization and Phoneme Decomposition (Kim et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.acl-srw.11.pdf