Exploring Methods for Building Dialects-Mandarin Code-Mixing Corpora: A Case Study in Taiwanese Hokkien

TL;DR

This paper presents a new approach for constructing a Hokkien-Mandarin code-mixed dataset, developing a linguistic toolkit for segmentation, and adapting a cross-lingual model to improve translation quality in dialect code-mixing scenarios.

Contribution

It introduces a novel dataset construction method, a linguistic toolkit for Hokkien segmentation, and an adapted XLM model for better dialect code-mixed translation.

Findings

Linguistic knowledge improves translation quality.

The dataset enables better code-mixed NLP research.

Adapted XLM performs well on dialect code-mixing tasks.

Abstract

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.