  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.
