Towards Explainable Khmer Polarity Classification
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915613016850432 |
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| author | Kong, Marry Buoy, Rina Chenda, Sovisal Taing, Nguonly |
| author_facet | Kong, Marry Buoy, Rina Chenda, Sovisal Taing, Nguonly |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_09313 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Explainable Khmer Polarity Classification Kong, Marry Buoy, Rina Chenda, Sovisal Taing, Nguonly Computation and Language 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. |
| title | Towards Explainable Khmer Polarity Classification |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2511.09313 |