RPEPL: Tibetan Sentiment Analysis Based on Relative Position Encoding and Prompt Learning

Sentiment analysis is a critical task for natural language processing. Much research has been done for high-resource languages such as English and Chinese. However, Tibetan is an extremely low resource language with less reference information. According to the practical demands, this article proposes RPEPL, a Tibetan sentiment analysis method based on relative position encoding and prompt learning. First, word information is introduced to syllable sequences by converting the directed acyclic lattice into a squashed structure. Second, a relative position encoding is used to encode the position information of syllables and words. Third, the association relations and semantic information of tokens are identified by leveraging the multi-attention. Finally, the sentiment category of the Tibetan sentence is obtained through a prompt learning framework. Experimental results demonstrate that RPEPL significantly outperforms the baseline methods on the TUSA dataset and TNEC (Tibetan News Event Comments) dataset. Additionally, traditional recurrent neural networks cannot perform large-scale parallel computation and convolutional neural networks have difficulty modeling long-distance dependencies in Tibetan text, both of which are resolved using RPEPL. Furthermore, the use of multi-attention not only enriches the association relations between syllables and words but also enhances the understanding of sentence semantic and syntactic structure information, and improves the performance of Tibetan sentiment analysis.