NICT‘s Submission To WAT 2020: How Effective Are Simple Many-To-Many Neural Machine Translation Models?
Abstract
In this paper we describe our team‘s (NICT-5) Neural Machine Translation (NMT) models whose translations were submitted to shared tasks of the 7th Workshop on Asian Translation. We participated in the Indic language multilingual sub-task as well as the NICT-SAP multilingual multi-domain sub-task. We focused on naive many-to-many NMT models which gave reasonable translation quality despite their simplicity. Our observations are twofold: (a.) Many-to-many models suffer from a lack of consistency where the translation quality for some language pairs is very good but for some others it is terrible when compared against one-to-many and many-to-one baselines. (b.) Oversampling smaller corpora does not necessarily give the best translation quality for the language pair associated with that pair.- Anthology ID:
- 2020.wat-1.9
- Volume:
- Proceedings of the 7th Workshop on Asian Translation
- Month:
- December
- Year:
- 2020
- Address:
- Suzhou, China
- Editors:
- Toshiaki Nakazawa, Hideki Nakayama, Chenchen Ding, Raj Dabre, Anoop Kunchukuttan, Win Pa Pa, Ondřej Bojar, Shantipriya Parida, Isao Goto, Hidaya Mino, Hiroshi Manabe, Katsuhito Sudoh, Sadao Kurohashi, Pushpak Bhattacharyya
- Venue:
- WAT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 98–102
- Language:
- URL:
- https://aclanthology.org/2020.wat-1.9
- DOI:
- Cite (ACL):
- Raj Dabre and Abhisek Chakrabarty. 2020. NICT‘s Submission To WAT 2020: How Effective Are Simple Many-To-Many Neural Machine Translation Models?. In Proceedings of the 7th Workshop on Asian Translation, pages 98–102, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- NICT‘s Submission To WAT 2020: How Effective Are Simple Many-To-Many Neural Machine Translation Models? (Dabre & Chakrabarty, WAT 2020)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/2020.wat-1.9.pdf