Abstract
In this paper, we present the NICT system (NICT-2) submitted to the NICT-SAP shared task at the 8th Workshop on Asian Translation (WAT-2021). A feature of our system is that we used a pretrained multilingual BART (Bidirectional and Auto-Regressive Transformer; mBART) model. Because publicly available models do not support some languages in the NICT-SAP task, we added these languages to the mBART model and then trained it using monolingual corpora extracted from Wikipedia. We fine-tuned the expanded mBART model using the parallel corpora specified by the NICT-SAP task. The BLEU scores greatly improved in comparison with those of systems without the pretrained model, including the additional languages.- Anthology ID:
- 2021.wat-1.8
- 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:
- 90–95
- Language:
- URL:
- https://aclanthology.org/2021.wat-1.8
- DOI:
- 10.18653/v1/2021.wat-1.8
- Cite (ACL):
- Kenji Imamura and Eiichiro Sumita. 2021. NICT-2 Translation System at WAT-2021: Applying a Pretrained Multilingual Encoder-Decoder Model to Low-resource Language Pairs. In Proceedings of the 8th Workshop on Asian Translation (WAT2021), pages 90–95, Online. Association for Computational Linguistics.
- Cite (Informal):
- NICT-2 Translation System at WAT-2021: Applying a Pretrained Multilingual Encoder-Decoder Model to Low-resource Language Pairs (Imamura & Sumita, WAT 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.wat-1.8.pdf