Exploring Methods for Building Dialects-Mandarin Code-Mixing Corpora: A Case Study in Taiwanese Hokkien
This addresses the problem of resource scarcity for dialect code-mixing in NLP, particularly for Chinese immigrants in Southeast Asia and Taiwan, but is incremental as it adapts existing methods to a specific language pair.
The paper tackled the challenge of code-mixing between dialects and Mandarin, specifically for Taiwanese Hokkien, by constructing a Hokkien-Mandarin dataset and using transfer learning with XLM for translation tasks, achieving good results on code-mixed data while maintaining monolingual translation quality.
In natural language processing (NLP), code-mixing (CM) is a challenging task, especially when the mixed languages include dialects. In Southeast Asian countries such as Singapore, Indonesia, and Malaysia, Hokkien-Mandarin is the most widespread code-mixed language pair among Chinese immigrants, and it is also common in Taiwan. However, dialects such as Hokkien often have a scarcity of resources and the lack of an official writing system, limiting the development of dialect CM research. In this paper, we propose a method to construct a Hokkien-Mandarin CM dataset to mitigate the limitation, overcome the morphological issue under the Sino-Tibetan language family, and offer an efficient Hokkien word segmentation method through a linguistics-based toolkit. Furthermore, we use our proposed dataset and employ transfer learning to train the XLM (cross-lingual language model) for translation tasks. To fit the code-mixing scenario, we adapt XLM slightly. We found that by using linguistic knowledge, rules, and language tags, the model produces good results on CM data translation while maintaining monolingual translation quality.