Dynamic Retrieval-Based Prompting for Cross-Lingual Dialogue Understanding in Persian | ||
| Journal of AI and Data Mining | ||
| دوره 14، شماره 2، تیر 2026، صفحه 183-196 اصل مقاله (939.07 K) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2025.16583.2785 | ||
| نویسندگان | ||
| Saedeh Tahery* ؛ Saeed Farzi | ||
| Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran | ||
| چکیده | ||
| Dialogue understanding for low-resource languages like Persian remains challenging due to limited annotated data, which constrains supervised training at scale. We propose a simple yet effective training-free method that combines machine translation, retrieval-based example selection, and prompting with a large language model (GPT-4o) to improve zero-shot cross-lingual performance. Given a Persian utterance translated into English, our method retrieves semantically and lexically similar English examples using a hybrid similarity function, translates them back into Persian, and constructs a few-shot prompt tailored to the input. This input-sensitive strategy enhances the quality of the examples, helping the model align more effectively with each instance. Experimental results on the Persian-ATIS dataset show that our approach improves intent detection and achieves competitive slot filling performance, outperforming state-of-the-art baselines without requiring any supervision in the target language. The modular pipeline is easy to reproduce and, in future work, can be extended to other low-resource languages, tasks, or retrieval configurations. The repository of our work is available at https://anonymous.4open.science/r/Persian_Language_Understanding-FDF4. | ||
| کلیدواژهها | ||
| Cross-lingual Adaptation؛ Natural Language Understanding؛ Persian Language؛ Large Language Models؛ ChatGPT | ||
| مراجع | ||
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