Knowledge-Guided Reinforcement Learning for Low-Resource Unsupervised Syllabification

Wenjun Wang, Ling Dong, Zhengtao Yu, Yuxin Huang, Junjun Guo, Shengxiang Gao

Abstract: Syllabification is a crucial task in natural language processing, and syllables also play a significant role as modeling units in speech processing. While deep learning methods have shown remarkable progress in syllabification, they face challenges in low-resource languages where ready-made segmentation datasets or rules are lacking. Large language models (LLMs) are mostly unsuitable for these low-resource languages as well. To address these challenges, this paper proposes an unsupervised syllabification approach that incorporates logical reasoning into the reinforcement learning training process, achieving knowledge-guided syllabification. By introducing logical reasoning knowledge and modeling the interaction between the Agent and the knowledge base (KB), the model gains a better understanding of language structures and patterns. The study primarily focuses on low-resource Lao language, with experiments conducted on a publicly available English dataset to validate the effectiveness of the proposed method.

External IDs:dblp:conf/ijcnn/WangDYHGG24