Abstract
Transliterating named entities from one language into another can be approached as neural machine translation (NMT) problem, for which we use deep attentional RNN encoder-decoder models. To build a strong transliteration system, we apply well-established techniques from NMT, such as dropout regularization, model ensembling, rescoring with right-to-left models, and back-translation. Our submission to the NEWS 2018 Shared Task on Named Entity Transliteration ranked first in several tracks.- Anthology ID:
- W18-2413
- Volume:
- Proceedings of the Seventh Named Entities Workshop
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- NEWS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 89–94
- Language:
- URL:
- https://aclanthology.org/W18-2413
- DOI:
- 10.18653/v1/W18-2413
- Cite (ACL):
- Roman Grundkiewicz and Kenneth Heafield. 2018. Neural Machine Translation Techniques for Named Entity Transliteration. In Proceedings of the Seventh Named Entities Workshop, pages 89–94, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Neural Machine Translation Techniques for Named Entity Transliteration (Grundkiewicz & Heafield, NEWS 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-2413.pdf
- Code
- snukky/news-translit-nmt