@inproceedings{wang-etal-2026-unified,
title = "A Unified Framework for Low-Resource Sawndip{--}{C}hinese Bidirectional Translation and Bilingual Dialogue Modeling",
author = "Wang, Zhengyuan and
Wu, Yixin and
Xu, Jingwen and
Huang, Zisu and
Lv, Changze and
Wang, Xiaohua and
Lu, Yiyang and
Tian, Muzhao and
Pan, Xiaosheng and
Zhang, Menghan and
Huang, Xuanjing and
Zheng, Xiaoqing",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1169/",
pages = "24748--24761",
ISBN = "979-8-89176-462-0",
abstract = "The decline of proficient practitioners poses an existential threat to numerous ancient writing systems, risking the loss of the cultural heritage they encode. Among these systems is Sawndip, a logographic script whose computational preservation faces severe challenges due to the scarcity of standardized corpora. In this paper, we present the first systematic framework for low-resource Sawndip-Chinese translation and bilingual conversational modeling. We introduce a newly curated dataset comprising sentence-level Sawndip-Chinese parallel data and character-level lexical definitions. Building upon this foundation, we propose a knowledge-enhanced supervised fine-tuning paradigm that incorporates lexical knowledge during training. To address the additional challenges of Chinese-to-Sawndip generation, we further develop a manually curated template-based substructure substitution strategy for synthesizing reverse-direction data. Bilingual conversational data is then constructed through a dimension-conditioned dialogue generation and multi-candidate back-translation pipeline, enabling controllable and diverse dialogue synthesis. Experimental results show that our final unified model effectively supports both Sawndip-Chinese translation and bilingual conversational abilities. Ultimately, this work provides a foundational reference for the digital preservation of Sawndip, offering broader insights into the computational documentation of globally-endangered languages."
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<abstract>The decline of proficient practitioners poses an existential threat to numerous ancient writing systems, risking the loss of the cultural heritage they encode. Among these systems is Sawndip, a logographic script whose computational preservation faces severe challenges due to the scarcity of standardized corpora. In this paper, we present the first systematic framework for low-resource Sawndip-Chinese translation and bilingual conversational modeling. We introduce a newly curated dataset comprising sentence-level Sawndip-Chinese parallel data and character-level lexical definitions. Building upon this foundation, we propose a knowledge-enhanced supervised fine-tuning paradigm that incorporates lexical knowledge during training. To address the additional challenges of Chinese-to-Sawndip generation, we further develop a manually curated template-based substructure substitution strategy for synthesizing reverse-direction data. Bilingual conversational data is then constructed through a dimension-conditioned dialogue generation and multi-candidate back-translation pipeline, enabling controllable and diverse dialogue synthesis. Experimental results show that our final unified model effectively supports both Sawndip-Chinese translation and bilingual conversational abilities. Ultimately, this work provides a foundational reference for the digital preservation of Sawndip, offering broader insights into the computational documentation of globally-endangered languages.</abstract>
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%0 Conference Proceedings
%T A Unified Framework for Low-Resource Sawndip–Chinese Bidirectional Translation and Bilingual Dialogue Modeling
%A Wang, Zhengyuan
%A Wu, Yixin
%A Xu, Jingwen
%A Huang, Zisu
%A Lv, Changze
%A Wang, Xiaohua
%A Lu, Yiyang
%A Tian, Muzhao
%A Pan, Xiaosheng
%A Zhang, Menghan
%A Huang, Xuanjing
%A Zheng, Xiaoqing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-unified
%X The decline of proficient practitioners poses an existential threat to numerous ancient writing systems, risking the loss of the cultural heritage they encode. Among these systems is Sawndip, a logographic script whose computational preservation faces severe challenges due to the scarcity of standardized corpora. In this paper, we present the first systematic framework for low-resource Sawndip-Chinese translation and bilingual conversational modeling. We introduce a newly curated dataset comprising sentence-level Sawndip-Chinese parallel data and character-level lexical definitions. Building upon this foundation, we propose a knowledge-enhanced supervised fine-tuning paradigm that incorporates lexical knowledge during training. To address the additional challenges of Chinese-to-Sawndip generation, we further develop a manually curated template-based substructure substitution strategy for synthesizing reverse-direction data. Bilingual conversational data is then constructed through a dimension-conditioned dialogue generation and multi-candidate back-translation pipeline, enabling controllable and diverse dialogue synthesis. Experimental results show that our final unified model effectively supports both Sawndip-Chinese translation and bilingual conversational abilities. Ultimately, this work provides a foundational reference for the digital preservation of Sawndip, offering broader insights into the computational documentation of globally-endangered languages.
%U https://aclanthology.org/2026.findings-emnlp.1169/
%P 24748-24761
Markdown (Informal)
[A Unified Framework for Low-Resource Sawndip–Chinese Bidirectional Translation and Bilingual Dialogue Modeling](https://aclanthology.org/2026.findings-emnlp.1169/) (Wang et al., Findings 2026)
ACL
- Zhengyuan Wang, Yixin Wu, Jingwen Xu, Zisu Huang, Changze Lv, Xiaohua Wang, Yiyang Lu, Muzhao Tian, Xiaosheng Pan, Menghan Zhang, Xuanjing Huang, and Xiaoqing Zheng. 2026. A Unified Framework for Low-Resource Sawndip–Chinese Bidirectional Translation and Bilingual Dialogue Modeling. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 24748–24761, Budapest, Hungary. Association for Computational Linguistics.