@inproceedings{alomair-etal-2026-wisdom,
title = "Wisdom in Unity: The Role of Multilingual Training in Figurative Language Identification in Proverbs",
author = "Alomair, Rama Khalid and
Alsubaie, Remas Hezam and
Mohammed, Walaa Saif Aleslam and
Alsonbul, Rima Khalid and
Alnajjar, Mona Saleh and
Aldosari, Razan Saeed and
Al-Ibrahim, Haya and
Aldayel, Abeer",
editor = "Adelani, David Ifeoluwa and
Alabi, Jesujoba and
Arnett, Catherine and
Chang, Tyler and
Downey, C.M. and
Goldman, Omer and
Li, Senyu and
Miranda, Lester James Validad and
Qi, Jirui and
Stap, David and
Winata, Genta Indra and
Zhang, Xinyu Crystina",
booktitle = "Proceedings of the 6th Workshop on Multilingual Representation Learning ({MRL} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.mrl-1.12/",
pages = "151--166",
ISBN = "979-8-89176-469-9",
abstract = "Although multilingual approaches to figurative language identification are not new, the shift beyond language-homogeneous training data requires a clearer understanding of the contribution of translated multilingual supervision. We examine this question using 742 proverb concepts across 6,787 translated instances for seven languages. We evaluate five models including multilingual encoders and instruction-tuned LLMs through progressively increasing levels of multilingual supervision. Moreover, we introduce multidimensional annotation framework for proverbs that characterizes proverbs through four complementary figurative forms: Metaphorical, Moral/Advisory, Cause{--}Effect, and Culture-Specific.\par Our findings show that overall, adding multilingual training data beyond 50{\%} provides only limited additional improvement, although the best supervision level varies across models and languages. Also, we show that combining diverse figurative forms yields the strongest overall performance. A notable finding is that the least frequent figurative form ``culture-specific'' exhibits the largest performance gains under multilingual supervision. Furthermore, the moral/advisory and culture-specific forms of proverb contribute more to instruct tuning LLM overall figurative identification performance. These findings motivate multilingual figurative identification to move beyond metaphor-centric taxonomies toward concept-level multidimensional frameworks that explicitly model complementary forms of figurative meanings that are context representative."
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<abstract>Although multilingual approaches to figurative language identification are not new, the shift beyond language-homogeneous training data requires a clearer understanding of the contribution of translated multilingual supervision. We examine this question using 742 proverb concepts across 6,787 translated instances for seven languages. We evaluate five models including multilingual encoders and instruction-tuned LLMs through progressively increasing levels of multilingual supervision. Moreover, we introduce multidimensional annotation framework for proverbs that characterizes proverbs through four complementary figurative forms: Metaphorical, Moral/Advisory, Cause–Effect, and Culture-Specific.\par Our findings show that overall, adding multilingual training data beyond 50% provides only limited additional improvement, although the best supervision level varies across models and languages. Also, we show that combining diverse figurative forms yields the strongest overall performance. A notable finding is that the least frequent figurative form “culture-specific” exhibits the largest performance gains under multilingual supervision. Furthermore, the moral/advisory and culture-specific forms of proverb contribute more to instruct tuning LLM overall figurative identification performance. These findings motivate multilingual figurative identification to move beyond metaphor-centric taxonomies toward concept-level multidimensional frameworks that explicitly model complementary forms of figurative meanings that are context representative.</abstract>
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%0 Conference Proceedings
%T Wisdom in Unity: The Role of Multilingual Training in Figurative Language Identification in Proverbs
%A Alomair, Rama Khalid
%A Alsubaie, Remas Hezam
%A Mohammed, Walaa Saif Aleslam
%A Alsonbul, Rima Khalid
%A Alnajjar, Mona Saleh
%A Aldosari, Razan Saeed
%A Al-Ibrahim, Haya
%A Aldayel, Abeer
%Y Adelani, David Ifeoluwa
%Y Alabi, Jesujoba
%Y Arnett, Catherine
%Y Chang, Tyler
%Y Downey, C. M.
%Y Goldman, Omer
%Y Li, Senyu
%Y Miranda, Lester James Validad
%Y Qi, Jirui
%Y Stap, David
%Y Winata, Genta Indra
%Y Zhang, Xinyu Crystina
%S Proceedings of the 6th Workshop on Multilingual Representation Learning (MRL 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-469-9
%F alomair-etal-2026-wisdom
%X Although multilingual approaches to figurative language identification are not new, the shift beyond language-homogeneous training data requires a clearer understanding of the contribution of translated multilingual supervision. We examine this question using 742 proverb concepts across 6,787 translated instances for seven languages. We evaluate five models including multilingual encoders and instruction-tuned LLMs through progressively increasing levels of multilingual supervision. Moreover, we introduce multidimensional annotation framework for proverbs that characterizes proverbs through four complementary figurative forms: Metaphorical, Moral/Advisory, Cause–Effect, and Culture-Specific.\par Our findings show that overall, adding multilingual training data beyond 50% provides only limited additional improvement, although the best supervision level varies across models and languages. Also, we show that combining diverse figurative forms yields the strongest overall performance. A notable finding is that the least frequent figurative form “culture-specific” exhibits the largest performance gains under multilingual supervision. Furthermore, the moral/advisory and culture-specific forms of proverb contribute more to instruct tuning LLM overall figurative identification performance. These findings motivate multilingual figurative identification to move beyond metaphor-centric taxonomies toward concept-level multidimensional frameworks that explicitly model complementary forms of figurative meanings that are context representative.
%U https://aclanthology.org/2026.mrl-1.12/
%P 151-166
Markdown (Informal)
[Wisdom in Unity: The Role of Multilingual Training in Figurative Language Identification in Proverbs](https://aclanthology.org/2026.mrl-1.12/) (Alomair et al., MRL 2026)
ACL
- Rama Khalid Alomair, Remas Hezam Alsubaie, Walaa Saif Aleslam Mohammed, Rima Khalid Alsonbul, Mona Saleh Alnajjar, Razan Saeed Aldosari, Haya Al-Ibrahim, and Abeer Aldayel. 2026. Wisdom in Unity: The Role of Multilingual Training in Figurative Language Identification in Proverbs. In Proceedings of the 6th Workshop on Multilingual Representation Learning (MRL 2026), pages 151–166, Budapest, Hungary. Association for Computational Linguistics.