Abstract
In this paper we describe our submission to the multilingual Indic language translation wtask “MultiIndicMT” under the team name “NICT-5”. This task involves translation from 10 Indic languages into English and vice-versa. The objective of the task was to explore the utility of multilingual approaches using a variety of in-domain and out-of-domain parallel and monolingual corpora. Given the recent success of multilingual NMT pre-training we decided to explore pre-training an MBART model on a large monolingual corpus collection covering all languages in this task followed by multilingual fine-tuning on small in-domain corpora. Firstly, we observed that a small amount of pre-training followed by fine-tuning on small bilingual corpora can yield large gains over when pre-training is not used. Furthermore, multilingual fine-tuning leads to further gains in translation quality which significantly outperforms a very strong multilingual baseline that does not rely on any pre-training.- Anthology ID:
- 2021.wat-1.23
- Volume:
- Proceedings of the 8th Workshop on Asian Translation (WAT2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- WAT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 198–204
- Language:
- URL:
- https://aclanthology.org/2021.wat-1.23
- DOI:
- 10.18653/v1/2021.wat-1.23
- Cite (ACL):
- Raj Dabre and Abhisek Chakrabarty. 2021. NICT-5’s Submission To WAT 2021: MBART Pre-training And In-Domain Fine Tuning For Indic Languages. In Proceedings of the 8th Workshop on Asian Translation (WAT2021), pages 198–204, Online. Association for Computational Linguistics.
- Cite (Informal):
- NICT-5’s Submission To WAT 2021: MBART Pre-training And In-Domain Fine Tuning For Indic Languages (Dabre & Chakrabarty, WAT 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.wat-1.23.pdf