Abstract
In this paper, we discuss the various techniques that we used to implement the Russian-Chinese machine translation system for the Triangular MT task at WMT 2021. Neural Machine translation systems based on transformer architecture have an encoder-decoder architecture, which are trained end-to-end and require a large amount of parallel corpus to produce good quality translations. This is the reason why neural machine translation systems are referred to as data hungry. Such a large amount of parallel corpus is majorly available for language pairs which include English and not for non-English language pairs. This is a major problem in building neural machine translation systems for non-English language pairs. We try to utilize the resources of the English language to improve the translation of non-English language pairs. We use the pivot language, that is English, to leverage transfer learning to improve the quality of Russian-Chinese translation. Compared to the baseline transformer-based neural machine translation system, we observe that the pivot language-based transfer learning technique gives a higher BLEU score.- Anthology ID:
- 2021.wmt-1.39
- Volume:
- Proceedings of the Sixth Conference on Machine Translation
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 336–340
- Language:
- URL:
- https://aclanthology.org/2021.wmt-1.39
- DOI:
- Cite (ACL):
- Shivam Mhaskar and Pushpak Bhattacharyya. 2021. Pivot Based Transfer Learning for Neural Machine Translation: CFILT IITB @ WMT 2021 Triangular MT. In Proceedings of the Sixth Conference on Machine Translation, pages 336–340, Online. Association for Computational Linguistics.
- Cite (Informal):
- Pivot Based Transfer Learning for Neural Machine Translation: CFILT IITB @ WMT 2021 Triangular MT (Mhaskar & Bhattacharyya, WMT 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.wmt-1.39.pdf