KIKIS at WMT 2025 General Translation Task

Koichi Iwakawa; Keito Kudo; Subaru Kimura; Takumi Ito; Jun Suzuki

KIKIS at WMT 2025 General Translation Task

Koichi Iwakawa, Keito Kudo, Subaru Kimura, Takumi Ito, Jun Suzuki

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Abstract

We participated in the constrained English–Japanese track of the WMT 2025 General Machine Translation Task.Our system collected the outputs produced by multiple subsystems, each of which consisted of LLM-based translation and reranking models configured differently (e.g., prompting strategies and context sizes), and reranked those outputs.Each subsystem generated multiple segment-level candidates and iteratively selected the most probable one to construct the document translation.We then reranked the document-level outputs from all subsystems to obtain the final translation.For reranking, we adopted a text-based LLM reranking approach with a reasoning model to take long contexts into account.Additionally, we built a bilingual dictionary on the fly from parallel corpora to make the system more robust to rare words.

Anthology ID:: 2025.wmt-1.47
Volume:: Proceedings of the Tenth Conference on Machine Translation
Month:: November
Year:: 2025
Address:: Suzhou, China
Editors:: Barry Haddow, Tom Kocmi, Philipp Koehn, Christof Monz
Venue:: WMT
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 705–722
Language:
URL:: https://aclanthology.org/2025.wmt-1.47/
DOI:
Bibkey:
Cite (ACL):: Koichi Iwakawa, Keito Kudo, Subaru Kimura, Takumi Ito, and Jun Suzuki. 2025. KIKIS at WMT 2025 General Translation Task. In Proceedings of the Tenth Conference on Machine Translation, pages 705–722, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):: KIKIS at WMT 2025 General Translation Task (Iwakawa et al., WMT 2025)
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PDF:: https://aclanthology.org/2025.wmt-1.47.pdf

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@inproceedings{iwakawa-etal-2025-kikis,
    title = "{KIKIS} at {WMT} 2025 General Translation Task",
    author = "Iwakawa, Koichi  and
      Kudo, Keito  and
      Kimura, Subaru  and
      Ito, Takumi  and
      Suzuki, Jun",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Tenth Conference on Machine Translation",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.wmt-1.47/",
    pages = "705--722",
    ISBN = "979-8-89176-341-8",
    abstract = "We participated in the constrained English{--}Japanese track of the WMT 2025 General Machine Translation Task.Our system collected the outputs produced by multiple subsystems, each of which consisted of LLM-based translation and reranking models configured differently (e.g., prompting strategies and context sizes), and reranked those outputs.Each subsystem generated multiple segment-level candidates and iteratively selected the most probable one to construct the document translation.We then reranked the document-level outputs from all subsystems to obtain the final translation.For reranking, we adopted a text-based LLM reranking approach with a reasoning model to take long contexts into account.Additionally, we built a bilingual dictionary on the fly from parallel corpora to make the system more robust to rare words."
}

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%0 Conference Proceedings
%T KIKIS at WMT 2025 General Translation Task
%A Iwakawa, Koichi
%A Kudo, Keito
%A Kimura, Subaru
%A Ito, Takumi
%A Suzuki, Jun
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Tenth Conference on Machine Translation
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-341-8
%F iwakawa-etal-2025-kikis
%X We participated in the constrained English–Japanese track of the WMT 2025 General Machine Translation Task.Our system collected the outputs produced by multiple subsystems, each of which consisted of LLM-based translation and reranking models configured differently (e.g., prompting strategies and context sizes), and reranked those outputs.Each subsystem generated multiple segment-level candidates and iteratively selected the most probable one to construct the document translation.We then reranked the document-level outputs from all subsystems to obtain the final translation.For reranking, we adopted a text-based LLM reranking approach with a reasoning model to take long contexts into account.Additionally, we built a bilingual dictionary on the fly from parallel corpora to make the system more robust to rare words.
%U https://aclanthology.org/2025.wmt-1.47/
%P 705-722

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Markdown (Informal)

[KIKIS at WMT 2025 General Translation Task](https://aclanthology.org/2025.wmt-1.47/) (Iwakawa et al., WMT 2025)

KIKIS at WMT 2025 General Translation Task (Iwakawa et al., WMT 2025)

ACL

Koichi Iwakawa, Keito Kudo, Subaru Kimura, Takumi Ito, and Jun Suzuki. 2025. KIKIS at WMT 2025 General Translation Task. In Proceedings of the Tenth Conference on Machine Translation, pages 705–722, Suzhou, China. Association for Computational Linguistics.