Abstract
We here describe our neural machine translation system for general machine translation shared task in WMT 2022. Our systems are based on the Transformer (Vaswani et al., 2017) with base settings. We explore the high-efficiency model training strategies, aimed to train a model with high-accuracy by using small model and a reasonable amount of data. We performed fine-tuning and ensembling with N-best ranking in English to/from Japanese directions. We found that fine-tuning by filtered JParaCrawl data set leads to better translations for both of direction in English to/from Japanese models. In English to Japanese direction model, ensembling and N-best ranking of 10 different checkpoints improved translations. By comparing with other online translation service, we found that our model achieved a great translation quality.- Anthology ID:
- 2022.wmt-1.22
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Philipp Koehn, Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Tom Kocmi, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Marco Turchi, Marcos Zampieri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 290–294
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.22
- DOI:
- Cite (ACL):
- Shivam Kalkar, Yoko Matsuzaki, and Ben Li. 2022. KYB General Machine Translation Systems for WMT22. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 290–294, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- KYB General Machine Translation Systems for WMT22 (Kalkar et al., WMT 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.wmt-1.22.pdf