@inproceedings{periti-etal-2025-definition,
title = "Definition Generation for Word Meaning Modeling: Monolingual, Multilingual, and Cross-Lingual Perspectives",
author = "Periti, Francesco and
Goworek, Roksana and
Dubossarsky, Haim and
Tahmasebi, Nina",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1321/",
pages = "26015--26035",
ISBN = "979-8-89176-332-6",
abstract = "The task of Definition Generation has recently gained attention as an interpretable approach to modeling word meaning. Thus far, most research has been conducted in English, with limited work and resources for other languages. In this work, we expand Definition Generation beyond English to a suite of 22 languages and evaluate Llama-based models within a monolingual, multilingual, and cross-lingual setting. Our experiments show that monolingual fine-tuning consistently outperforms pretrained baselines, with the largest gains observed in languages with lower initial performance; and that multilingual fine-tuning does not consistently improve performance on the individual fine-tuning languages. Our cross-lingual evaluation reveals that models fine-tuned on a single language typically lose the ability to generate definitions in other languages, whereas multilingual models exhibit robust generalization even to languages unseen during fine-tuning."
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<abstract>The task of Definition Generation has recently gained attention as an interpretable approach to modeling word meaning. Thus far, most research has been conducted in English, with limited work and resources for other languages. In this work, we expand Definition Generation beyond English to a suite of 22 languages and evaluate Llama-based models within a monolingual, multilingual, and cross-lingual setting. Our experiments show that monolingual fine-tuning consistently outperforms pretrained baselines, with the largest gains observed in languages with lower initial performance; and that multilingual fine-tuning does not consistently improve performance on the individual fine-tuning languages. Our cross-lingual evaluation reveals that models fine-tuned on a single language typically lose the ability to generate definitions in other languages, whereas multilingual models exhibit robust generalization even to languages unseen during fine-tuning.</abstract>
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%0 Conference Proceedings
%T Definition Generation for Word Meaning Modeling: Monolingual, Multilingual, and Cross-Lingual Perspectives
%A Periti, Francesco
%A Goworek, Roksana
%A Dubossarsky, Haim
%A Tahmasebi, Nina
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F periti-etal-2025-definition
%X The task of Definition Generation has recently gained attention as an interpretable approach to modeling word meaning. Thus far, most research has been conducted in English, with limited work and resources for other languages. In this work, we expand Definition Generation beyond English to a suite of 22 languages and evaluate Llama-based models within a monolingual, multilingual, and cross-lingual setting. Our experiments show that monolingual fine-tuning consistently outperforms pretrained baselines, with the largest gains observed in languages with lower initial performance; and that multilingual fine-tuning does not consistently improve performance on the individual fine-tuning languages. Our cross-lingual evaluation reveals that models fine-tuned on a single language typically lose the ability to generate definitions in other languages, whereas multilingual models exhibit robust generalization even to languages unseen during fine-tuning.
%U https://aclanthology.org/2025.emnlp-main.1321/
%P 26015-26035
Markdown (Informal)
[Definition Generation for Word Meaning Modeling: Monolingual, Multilingual, and Cross-Lingual Perspectives](https://aclanthology.org/2025.emnlp-main.1321/) (Periti et al., EMNLP 2025)
ACL