@inproceedings{dhawan-etal-2026-gators,
title = "gators: {BM}25-Augmented Many-Shot Translation for Low-Resource North-{E}astern {I}ndian Languages",
author = "Dhawan, Aashish and
Driggers-Ellis, Christopher and
Kasinets, Dzmitry and
Grant, Christan and
Wang, Zhe",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Eleventh Conference on Machine Translation",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wmt-main.150/",
pages = "2111--2123",
ISBN = "979-8-89176-474-3",
abstract = "This paper describes the University of Florida gators submission to the WMT26 Low-Resource Indic Language Translation shared task. We adapt the retrieval-augmented many-shot translation pipeline from our AmericasNLP 2026 system to translate between English and eleven North-Eastern Indian languages in both directions. At inference time, BM25 retrieves the most similar parallel examples from a language-specific training bank, and Gemini 2.5 Flash translates the input conditioned on these examples. No model fine-tuning is involved. Training banks combine official WMT26 data with publicly available corpora such as Samanantar and prior WMT shared task releases. A grid search over retrieval count r and development exemplar count d across all 22 language-direction pairs selects the best configuration for each submission."
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<abstract>This paper describes the University of Florida gators submission to the WMT26 Low-Resource Indic Language Translation shared task. We adapt the retrieval-augmented many-shot translation pipeline from our AmericasNLP 2026 system to translate between English and eleven North-Eastern Indian languages in both directions. At inference time, BM25 retrieves the most similar parallel examples from a language-specific training bank, and Gemini 2.5 Flash translates the input conditioned on these examples. No model fine-tuning is involved. Training banks combine official WMT26 data with publicly available corpora such as Samanantar and prior WMT shared task releases. A grid search over retrieval count r and development exemplar count d across all 22 language-direction pairs selects the best configuration for each submission.</abstract>
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%0 Conference Proceedings
%T gators: BM25-Augmented Many-Shot Translation for Low-Resource North-Eastern Indian Languages
%A Dhawan, Aashish
%A Driggers-Ellis, Christopher
%A Kasinets, Dzmitry
%A Grant, Christan
%A Wang, Zhe
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F dhawan-etal-2026-gators
%X This paper describes the University of Florida gators submission to the WMT26 Low-Resource Indic Language Translation shared task. We adapt the retrieval-augmented many-shot translation pipeline from our AmericasNLP 2026 system to translate between English and eleven North-Eastern Indian languages in both directions. At inference time, BM25 retrieves the most similar parallel examples from a language-specific training bank, and Gemini 2.5 Flash translates the input conditioned on these examples. No model fine-tuning is involved. Training banks combine official WMT26 data with publicly available corpora such as Samanantar and prior WMT shared task releases. A grid search over retrieval count r and development exemplar count d across all 22 language-direction pairs selects the best configuration for each submission.
%U https://aclanthology.org/2026.wmt-main.150/
%P 2111-2123
Markdown (Informal)
[gators: BM25-Augmented Many-Shot Translation for Low-Resource North-Eastern Indian Languages](https://aclanthology.org/2026.wmt-main.150/) (Dhawan et al., WMT 2026)
ACL