@inproceedings{vazquez-risco-torres-2025-prompt,
    title = "Prompt-based Language Generation for Complex Conversational Coaching Tasks across Languages",
    author = "Vazquez Risco, Alain  and
      Torres, Maria Ines",
    editor = "B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Lef{\`e}vre, Fabrice  and
      Asher, Nicholas  and
      Kim, Seokhwan  and
      Merlin, Teva",
    booktitle = "Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = aug,
    year = "2025",
    address = "Avignon, France",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-10/2025.sigdial-1.48/",
    pages = "601--608",
    abstract = "We investigate the role of prompt-based demonstrators in improving natural language generation for coaching-oriented dialogue systems in different languages. These systems present significant challenges due to their need for semantically accurate, goal-driven responses across diverse dialogue act taxonomies and languages. We define three types of prompt demonstrators, i.e., pairs of meaning representation-utterance, that include different degrees of specification in such meaning representation. We then fine-tune pretrained language models separately for four very different languages and evaluate how the specificity of these demonstrators affects the quality of the generated sentences. Our experiments show that more specific prompts lead to more coherent and accurate outputs, particularly for low-resource languages and small models. Additionally, we observe promising zero-shot performance with larger models, showing a complementary value of prompts. These results demonstrate that simple prompting strategies, combined with fine-tuning, can significantly improve output quality in complex dialogue generation tasks across languages."
}