@inproceedings{ramos-etal-2025-grammamt,
title = "{G}ramma{MT}: Improving Machine Translation with Grammar-Informed In-Context Learning",
author = "Ramos, Rita and
Chimoto, Everlyn Asiko and
Ter Hoeve, Maartje and
Schluter, Natalie",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1447/",
doi = "10.18653/v1/2025.acl-long.1447",
pages = "29920--29940",
ISBN = "979-8-89176-251-0",
abstract = "We introduce GrammaMT, a grammatically-aware prompting approach for machine translation that uses Interlinear Glossed Text (IGT), a common form of linguistic description providing morphological and lexical annotations for source sentences. GrammaMT proposes three prompting strategies: gloss-shot, chain-gloss and model-gloss. All are training-free, requiring only a few examples that involve minimal effort to collect, and making them well-suited for low-resource setups. Experiments show that GrammaMT enhances translation performance on open-source instruction-tuned LLMs for various low- to high-resource languages across three benchmarks: (1) the largest IGT corpus, (2) the challenging 2023 SIGMORPHON Shared Task data over endangered languages, and (3) even in an out-of-domain setting with FLORES. Moreover, ablation studies reveal that leveraging gloss resources could substantially boost MT performance (by over 17 BLEU points) if LLMs accurately generate or access input sentence glosses."
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%0 Conference Proceedings
%T GrammaMT: Improving Machine Translation with Grammar-Informed In-Context Learning
%A Ramos, Rita
%A Chimoto, Everlyn Asiko
%A Ter Hoeve, Maartje
%A Schluter, Natalie
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F ramos-etal-2025-grammamt
%X We introduce GrammaMT, a grammatically-aware prompting approach for machine translation that uses Interlinear Glossed Text (IGT), a common form of linguistic description providing morphological and lexical annotations for source sentences. GrammaMT proposes three prompting strategies: gloss-shot, chain-gloss and model-gloss. All are training-free, requiring only a few examples that involve minimal effort to collect, and making them well-suited for low-resource setups. Experiments show that GrammaMT enhances translation performance on open-source instruction-tuned LLMs for various low- to high-resource languages across three benchmarks: (1) the largest IGT corpus, (2) the challenging 2023 SIGMORPHON Shared Task data over endangered languages, and (3) even in an out-of-domain setting with FLORES. Moreover, ablation studies reveal that leveraging gloss resources could substantially boost MT performance (by over 17 BLEU points) if LLMs accurately generate or access input sentence glosses.
%R 10.18653/v1/2025.acl-long.1447
%U https://aclanthology.org/2025.acl-long.1447/
%U https://doi.org/10.18653/v1/2025.acl-long.1447
%P 29920-29940
Markdown (Informal)
[GrammaMT: Improving Machine Translation with Grammar-Informed In-Context Learning](https://aclanthology.org/2025.acl-long.1447/) (Ramos et al., ACL 2025)
ACL