Investigating Discourse Segmentation in Taiwan Southern Min Spontaneous Speech
Résumé
In recent years, discourse segmentation has received increased attention; however the majority of studies have focused on written genres and languages with abundant linguistic resources. This paper investigates discourse segmentation of a spontaneous speech corpus in Taiwan Southern Min. We compare finetuning a Language Model (LLM) using two approaches: supervised, taking advantage of a high-quality annotated dataset, and weaklysupervised, which requires only a small amount of manual labeling. The corpus used here is transcribed in both Chinese characters and romanized script. This allows us to assess the impact of the written form on the discourse segmentation task. Moreover, the dataset includes manual prosodic break labeling, allowing an exploration of the role prosody can play in contemporary discourse segmentation systems grounded in LLMs. In our study, the supervised approach outperforms weak-supervision; the character-based version demonstrates better scores compared to the romanized version; and prosodic information proves to be an interesting source to increase discourse segmentation performance.
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