Year: 2022
Type: dissertation
Abstract: Thai sentence segmentation has been on the topic of interest among Thai NLP communities. However, not much literature has explored the use of transformer-based large language models to tackle the issue. We conduct three experiments on the LST20 corpus, including (1) fine-tuning WangchanBERTa, a large language model pre-trained on Thai, across different classification tasks, (2) joint learning for clause and sentence segmentation, and (3) cross-lingual transfer using the multilingual model XLM-RoBERTa. Our findings show that WangchanBERTa outperforms other models in Thai sentence segmentation, and fine-tuning it with token and contextual information further improves its performance. However, cross-lingual transfer from English and Chinese to Thai is not effective for this task.
Source: Chula Digiverse
Authors: Narongkorn Panitsrisit, Attapol Thamrongrattanarit
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4386784623