@inproceedings{rikters-miwa-2024-aist,
title = "{AIST} {AIRC} Systems for the {WMT} 2024 Shared Tasks",
author = "Rikters, Matiss and
Miwa, Makoto",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Ninth Conference on Machine Translation",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wmt-1.22",
doi = "10.18653/v1/2024.wmt-1.22",
pages = "286--291",
abstract = "At WMT 2024 AIST AIRC participated in the General Machine Translation shared task and the Biomedical Translation task. We trained constrained track models for translation between English, German, and Japanese. Before training the final models, we first filtered the parallel data, then performed iterative back-translation as well as parallel data distillation. We experimented with training baseline Transformer models, Mega models, and fine-tuning open-source T5 and Gemma model checkpoints using the filtered parallel data. Our primary submissions contain translations from ensembles of two Mega model checkpoints and our contrastive submissions are generated by our fine-tuned T5 model checkpoints.",
}
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%0 Conference Proceedings
%T AIST AIRC Systems for the WMT 2024 Shared Tasks
%A Rikters, Matiss
%A Miwa, Makoto
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F rikters-miwa-2024-aist
%X At WMT 2024 AIST AIRC participated in the General Machine Translation shared task and the Biomedical Translation task. We trained constrained track models for translation between English, German, and Japanese. Before training the final models, we first filtered the parallel data, then performed iterative back-translation as well as parallel data distillation. We experimented with training baseline Transformer models, Mega models, and fine-tuning open-source T5 and Gemma model checkpoints using the filtered parallel data. Our primary submissions contain translations from ensembles of two Mega model checkpoints and our contrastive submissions are generated by our fine-tuned T5 model checkpoints.
%R 10.18653/v1/2024.wmt-1.22
%U https://aclanthology.org/2024.wmt-1.22
%U https://doi.org/10.18653/v1/2024.wmt-1.22
%P 286-291
Markdown (Informal)
[AIST AIRC Systems for the WMT 2024 Shared Tasks](https://aclanthology.org/2024.wmt-1.22) (Rikters & Miwa, WMT 2024)
ACL