SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models
Abdul Khan, Hrishikesh Kanade, Girish Budhrani, Preet Jhanglani, Jia Xu
Abstract
This paper describes the Stevens Institute of Technology’s submission for the WMT 2022 Shared Task: Code-mixed Machine Translation (MixMT). The task consisted of two subtasks, subtask 1 Hindi/English to Hinglish and subtask 2 Hinglish to English translation. Our findings lie in the improvements made through the use of large pre-trained multilingual NMT models and in-domain datasets, as well as back-translation and ensemble techniques. The translation output is automatically evaluated against the reference translations using ROUGE-L and WER. Our system achieves the 1st position on subtask 2 according to ROUGE-L, WER, and human evaluation, 1st position on subtask 1 according to WER and human evaluation, and 3rd position on subtask 1 with respect to ROUGE-L metric.- Anthology ID:
- 2022.wmt-1.114
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Venue:
- WMT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1136–1144
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.114
- DOI:
- Cite (ACL):
- Abdul Khan, Hrishikesh Kanade, Girish Budhrani, Preet Jhanglani, and Jia Xu. 2022. SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 1136–1144, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- SIT at MixMT 2022: Fluent Translation Built on Giant Pre-trained Models (Khan et al., WMT 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.wmt-1.114.pdf