Incorporating Transformer Models for Sentiment Analysis and News Classification in Khmer

Abstract

In recent years, natural language modeling has achieved a major breakthrough with its sophisticated theoretical and technical advancements. Leveraging the power of deep learning, transformer models have created a disrupting impact in the domain of natural language processing. However, the benefits of such advancements are still inscribed between few highly resourced languages such as English, German, and French. Low-resourced language such as Khmer is still deprived of utilizing these advancements due to lack of technical support for this language. In this study, our objective is to apply the state-of-the-art language models within two empirical use cases such as Sentiment Analysis and News Classification in the Khmer language. To perform the classification tasks, we have employed FastText and BERT for extracting word embeddings and carried out three different type of experiments such as FastText, BERT feature-based, and BERT fine-tuning-based. A large text corpus including over 100,000 news articles has been used for pre-training the transformer model, BERT. The outcome of our experiment shows that in both of the use cases, a pre-trained and fine-tuned BERT model produces the outperforming results.