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        <title>SinFoS: A Parallel Dataset for Translating Sinhala Figures of Speech</title>
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            <title>Proceedings of the 22nd Workshop on Multiword Expressions (MWE 2026)</title>
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    <abstract>Figures of Speech (FOS) consist of multi-word phrases that are deeply intertwined with culture. While Neural Machine Translation (NMT) performs relatively well with the figurative expressions of high-resource languages, it often faces challenges when dealing with low-resource languages like Sinhala due to limited available data. To address this limitation, we introduce a corpus of 2,344 Sinhala figures of speech with cultural and cross-lingual annotations. We examine this dataset to classify the cultural origins of the figures of speech and to identify their cross-lingual equivalents. Additionally, we have developed a binary classifier to differentiate between two types of FOS in the dataset, achieving an accuracy rate of approximately 92%. We also evaluate the performance of existing LLMs on this dataset. Our findings reveal significant shortcomings in the current capabilities of LLMs, as these models often struggle to accurately convey idiomatic meanings. By making this dataset publicly available, we offer a crucial benchmark for future research in low-resource NLP and culturally aware machine translation.</abstract>
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%0 Conference Proceedings
%T SinFoS: A Parallel Dataset for Translating Sinhala Figures of Speech
%A Sofalas, Johan Nevin
%A Pavithra, Dilushri
%A Jayatilleke, Nevidu
%A Weerasinghe, Ruvan
%Y Ojha, Atul Kr.
%Y Mititelu, Verginica Barbu
%Y Constant, Mathieu
%Y Stoyanova, Ivelina
%Y Doğruöz, A. Seza
%Y Rademaker, Alexandre
%S Proceedings of the 22nd Workshop on Multiword Expressions (MWE 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Marocco
%@ 979-8-89176-363-0
%F sofalas-etal-2026-sinfos
%X Figures of Speech (FOS) consist of multi-word phrases that are deeply intertwined with culture. While Neural Machine Translation (NMT) performs relatively well with the figurative expressions of high-resource languages, it often faces challenges when dealing with low-resource languages like Sinhala due to limited available data. To address this limitation, we introduce a corpus of 2,344 Sinhala figures of speech with cultural and cross-lingual annotations. We examine this dataset to classify the cultural origins of the figures of speech and to identify their cross-lingual equivalents. Additionally, we have developed a binary classifier to differentiate between two types of FOS in the dataset, achieving an accuracy rate of approximately 92%. We also evaluate the performance of existing LLMs on this dataset. Our findings reveal significant shortcomings in the current capabilities of LLMs, as these models often struggle to accurately convey idiomatic meanings. By making this dataset publicly available, we offer a crucial benchmark for future research in low-resource NLP and culturally aware machine translation.
%U https://aclanthology.org/2026.mwe-1.2/
%P 8-26

Markdown (Informal)

[SinFoS: A Parallel Dataset for Translating Sinhala Figures of Speech](https://aclanthology.org/2026.mwe-1.2/) (Sofalas et al., MWE 2026)

• SinFoS: A Parallel Dataset for Translating Sinhala Figures of Speech (Sofalas et al., MWE 2026)

ACL

• Johan Nevin Sofalas, Dilushri Pavithra, Nevidu Jayatilleke, and Ruvan Weerasinghe. 2026. SinFoS: A Parallel Dataset for Translating Sinhala Figures of Speech. In Proceedings of the 22nd Workshop on Multiword Expressions (MWE 2026), pages 8–26, Rabat, Marocco. Association for Computational Linguistics.