@inproceedings{liang-etal-2025-colloquial,
title = "Colloquial Singaporean {E}nglish Style Transfer with Fine-Grained Explainable Control",
author = "Liang, Jinggui and
Vo, Dung and
Xian, Yap Hong and
Chieu, Hai Leong and
Chai, Kian Ming A. and
Jiang, Jing and
Liao, Lizi",
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.1309/",
doi = "10.18653/v1/2025.acl-long.1309",
pages = "26962--26983",
ISBN = "979-8-89176-251-0",
abstract = "Colloquial Singaporean English (Singlish) is an informal English marked by a unique blend of languages reflecting Singapore{'}s multicultural identity. Style transfer between Singlish and Standard (formal) English is vital for various applications, yet existing methods often lack explainability and fine-grained control. To fill this gap, we contribute in two key ways. First, we construct a large, high-quality dataset of formal and informal sentences, annotated across six linguistic aspects{---}Syntax, Lexical Borrowing, Pragmatics, Prosody/Phonology, Emoticons/Punctuation, and Code-Switching{---}with detailed explanations. Starting with manually annotated cases, we scaled the dataset to 140K with ensured quality. Second, inspired by the ``Society of Mind'' theory, we propose a novel multi-agent framework where large language models (LLMs) act as expert agents for each linguistic aspect. These agents collaborate by iteratively generating, critiquing, and refining responses to achieve controlled, explainable style transfer. Both automatic metrics and human evaluations confirm that our method enables precise, interpretable transformations, advancing explainability in NLP for Singlish."
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<abstract>Colloquial Singaporean English (Singlish) is an informal English marked by a unique blend of languages reflecting Singapore’s multicultural identity. Style transfer between Singlish and Standard (formal) English is vital for various applications, yet existing methods often lack explainability and fine-grained control. To fill this gap, we contribute in two key ways. First, we construct a large, high-quality dataset of formal and informal sentences, annotated across six linguistic aspects—Syntax, Lexical Borrowing, Pragmatics, Prosody/Phonology, Emoticons/Punctuation, and Code-Switching—with detailed explanations. Starting with manually annotated cases, we scaled the dataset to 140K with ensured quality. Second, inspired by the “Society of Mind” theory, we propose a novel multi-agent framework where large language models (LLMs) act as expert agents for each linguistic aspect. These agents collaborate by iteratively generating, critiquing, and refining responses to achieve controlled, explainable style transfer. Both automatic metrics and human evaluations confirm that our method enables precise, interpretable transformations, advancing explainability in NLP for Singlish.</abstract>
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%0 Conference Proceedings
%T Colloquial Singaporean English Style Transfer with Fine-Grained Explainable Control
%A Liang, Jinggui
%A Vo, Dung
%A Xian, Yap Hong
%A Chieu, Hai Leong
%A Chai, Kian Ming A.
%A Jiang, Jing
%A Liao, Lizi
%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 liang-etal-2025-colloquial
%X Colloquial Singaporean English (Singlish) is an informal English marked by a unique blend of languages reflecting Singapore’s multicultural identity. Style transfer between Singlish and Standard (formal) English is vital for various applications, yet existing methods often lack explainability and fine-grained control. To fill this gap, we contribute in two key ways. First, we construct a large, high-quality dataset of formal and informal sentences, annotated across six linguistic aspects—Syntax, Lexical Borrowing, Pragmatics, Prosody/Phonology, Emoticons/Punctuation, and Code-Switching—with detailed explanations. Starting with manually annotated cases, we scaled the dataset to 140K with ensured quality. Second, inspired by the “Society of Mind” theory, we propose a novel multi-agent framework where large language models (LLMs) act as expert agents for each linguistic aspect. These agents collaborate by iteratively generating, critiquing, and refining responses to achieve controlled, explainable style transfer. Both automatic metrics and human evaluations confirm that our method enables precise, interpretable transformations, advancing explainability in NLP for Singlish.
%R 10.18653/v1/2025.acl-long.1309
%U https://aclanthology.org/2025.acl-long.1309/
%U https://doi.org/10.18653/v1/2025.acl-long.1309
%P 26962-26983
Markdown (Informal)
[Colloquial Singaporean English Style Transfer with Fine-Grained Explainable Control](https://aclanthology.org/2025.acl-long.1309/) (Liang et al., ACL 2025)
ACL