@inproceedings{zhang-etal-2025-read,
title = "Read it in Two Steps: Translating Extremely Low-Resource Languages with Code-Augmented Grammar Books",
author = "Zhang, Chen and
Lin, Jiuheng and
Liu, Xiao and
Zhang, Zekai and
Feng, Yansong",
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.202/",
doi = "10.18653/v1/2025.acl-long.202",
pages = "3977--3997",
ISBN = "979-8-89176-251-0",
abstract = "While large language models (LLMs) have shown promise in translating extremely low-resource languages using resources like dictionaries, the effectiveness of grammar books remains debated. This paper investigates the role of grammar books in translating extremely low-resource languages by decomposing it into two key steps: grammar rule retrieval and application. To facilitate the study, we introduce ZhuangRules, a modularized dataset of grammar rules and their corresponding test sentences. Our analysis reveals that rule retrieval constitutes a primary bottleneck in grammar-based translation. Moreover, although LLMs can apply simple rules for translation when explicitly provided, they encounter difficulties in handling more complex rules. To address these challenges, we propose representing grammar rules as code functions, considering their similarities in structure and the benefit of code in facilitating LLM reasoning. Our experiments show that using code rules significantly boosts both rule retrieval and application, ultimately resulting in a 13.1{\%} BLEU improvement in translation."
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%0 Conference Proceedings
%T Read it in Two Steps: Translating Extremely Low-Resource Languages with Code-Augmented Grammar Books
%A Zhang, Chen
%A Lin, Jiuheng
%A Liu, Xiao
%A Zhang, Zekai
%A Feng, Yansong
%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 zhang-etal-2025-read
%X While large language models (LLMs) have shown promise in translating extremely low-resource languages using resources like dictionaries, the effectiveness of grammar books remains debated. This paper investigates the role of grammar books in translating extremely low-resource languages by decomposing it into two key steps: grammar rule retrieval and application. To facilitate the study, we introduce ZhuangRules, a modularized dataset of grammar rules and their corresponding test sentences. Our analysis reveals that rule retrieval constitutes a primary bottleneck in grammar-based translation. Moreover, although LLMs can apply simple rules for translation when explicitly provided, they encounter difficulties in handling more complex rules. To address these challenges, we propose representing grammar rules as code functions, considering their similarities in structure and the benefit of code in facilitating LLM reasoning. Our experiments show that using code rules significantly boosts both rule retrieval and application, ultimately resulting in a 13.1% BLEU improvement in translation.
%R 10.18653/v1/2025.acl-long.202
%U https://aclanthology.org/2025.acl-long.202/
%U https://doi.org/10.18653/v1/2025.acl-long.202
%P 3977-3997
Markdown (Informal)
[Read it in Two Steps: Translating Extremely Low-Resource Languages with Code-Augmented Grammar Books](https://aclanthology.org/2025.acl-long.202/) (Zhang et al., ACL 2025)
ACL