@inproceedings{rezaeimanesh-etal-2025-large,
title = "Large Language Models for {P}ersian-{E}nglish Idiom Translation",
author = "Rezaeimanesh, Sara and
Hosseini, Faezeh and
Yaghoobzadeh, Yadollah",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.405/",
doi = "10.18653/v1/2025.naacl-long.405",
pages = "7974--7985",
ISBN = "979-8-89176-189-6",
abstract = "Large language models (LLMs) have shown superior capabilities in translating figurative language compared to neural machine translation (NMT) systems. However, the impact of different prompting methods and LLM-NMT combinations on idiom translation has yet to be thoroughly investigated. This paper introduces two parallel datasets of sentences containing idiomatic expressions for Persian$\rightarrow$English and English$\rightarrow$Persian translations, with Persian idioms sampled from our PersianIdioms resource, a collection of 2,200 idioms and their meanings, with 700 including usage examples.Using these datasets, we evaluate various open- and closed-source LLMs, NMT models, and their combinations. Translation quality is assessed through idiom translation accuracy and fluency. We also find that automatic evaluation methods like LLM-as-a-judge, BLEU, and BERTScore are effective for comparing different aspects of model performance. Our experiments reveal that Claude-3.5-Sonnet delivers outstanding results in both translation directions. For English$\rightarrow$Persian, combining weaker LLMs with Google Translate improves results, while Persian$\rightarrow$English translations benefit from single prompts for simpler models and complex prompts for advanced ones."
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<abstract>Large language models (LLMs) have shown superior capabilities in translating figurative language compared to neural machine translation (NMT) systems. However, the impact of different prompting methods and LLM-NMT combinations on idiom translation has yet to be thoroughly investigated. This paper introduces two parallel datasets of sentences containing idiomatic expressions for Persian\rightarrowEnglish and English\rightarrowPersian translations, with Persian idioms sampled from our PersianIdioms resource, a collection of 2,200 idioms and their meanings, with 700 including usage examples.Using these datasets, we evaluate various open- and closed-source LLMs, NMT models, and their combinations. Translation quality is assessed through idiom translation accuracy and fluency. We also find that automatic evaluation methods like LLM-as-a-judge, BLEU, and BERTScore are effective for comparing different aspects of model performance. Our experiments reveal that Claude-3.5-Sonnet delivers outstanding results in both translation directions. For English\rightarrowPersian, combining weaker LLMs with Google Translate improves results, while Persian\rightarrowEnglish translations benefit from single prompts for simpler models and complex prompts for advanced ones.</abstract>
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%0 Conference Proceedings
%T Large Language Models for Persian-English Idiom Translation
%A Rezaeimanesh, Sara
%A Hosseini, Faezeh
%A Yaghoobzadeh, Yadollah
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F rezaeimanesh-etal-2025-large
%X Large language models (LLMs) have shown superior capabilities in translating figurative language compared to neural machine translation (NMT) systems. However, the impact of different prompting methods and LLM-NMT combinations on idiom translation has yet to be thoroughly investigated. This paper introduces two parallel datasets of sentences containing idiomatic expressions for Persian\rightarrowEnglish and English\rightarrowPersian translations, with Persian idioms sampled from our PersianIdioms resource, a collection of 2,200 idioms and their meanings, with 700 including usage examples.Using these datasets, we evaluate various open- and closed-source LLMs, NMT models, and their combinations. Translation quality is assessed through idiom translation accuracy and fluency. We also find that automatic evaluation methods like LLM-as-a-judge, BLEU, and BERTScore are effective for comparing different aspects of model performance. Our experiments reveal that Claude-3.5-Sonnet delivers outstanding results in both translation directions. For English\rightarrowPersian, combining weaker LLMs with Google Translate improves results, while Persian\rightarrowEnglish translations benefit from single prompts for simpler models and complex prompts for advanced ones.
%R 10.18653/v1/2025.naacl-long.405
%U https://aclanthology.org/2025.naacl-long.405/
%U https://doi.org/10.18653/v1/2025.naacl-long.405
%P 7974-7985
Markdown (Informal)
[Large Language Models for Persian-English Idiom Translation](https://aclanthology.org/2025.naacl-long.405/) (Rezaeimanesh et al., NAACL 2025)
ACL
- Sara Rezaeimanesh, Faezeh Hosseini, and Yadollah Yaghoobzadeh. 2025. Large Language Models for Persian-English Idiom Translation. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 7974–7985, Albuquerque, New Mexico. Association for Computational Linguistics.