Towards Explainable Khmer Polarity Classification
This work addresses the lack of explainability in existing Khmer polarity classification models, which is an incremental improvement for natural language processing in the Khmer language domain.
The paper tackled the problem of Khmer polarity classification by proposing an explainable classifier that fine-tunes a Qwen-3 model to predict labels and provide self-explanations, resulting in accurate predictions with reasoning based on polarity-related keywords or phrases.
Khmer polarity classification is a fundamental natural language processing task that assigns a positive, negative, or neutral label to a given Khmer text input. Existing Khmer models typically predict the label without explaining the rationale behind the prediction. This paper proposes an explainable Khmer polarity classifier by fine-tuning an instruction-based reasoning Qwen-3 model. The notion of explainability in this paper is limited to self-explanations, which the model uses to rationalize its predictions. Experimental results show that the fine-tuned model not only predicts labels accurately but also provides reasoning by identifying polarity-related keywords or phrases to support its predictions. In addition, we contribute a new Khmer polarity dataset consisting of short- to medium-length casual, romanized, and mixed-code Khmer expressions. This dataset was constructed using both heuristic rules and human curation and is publicly available through a gated Hugging Face repository (rinabuoy/khmerpolarity_nonreasoning). The fine-tuned Qwen-3 models are also made available in the same Hugging Face account.