@inproceedings{labruna-magnini-2023-addressing,
    title = "Addressing Domain Changes in Task-oriented Conversational Agents through Dialogue Adaptation",
    author = "Labruna, Tiziano  and
      Magnini, Bernardo",
    editor = "Bassignana, Elisa  and
      Lindemann, Matthias  and
      Petit, Alban",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2023.eacl-srw.16/",
    doi = "10.18653/v1/2023.eacl-srw.16",
    pages = "149--158",
    abstract = "Recent task-oriented dialogue systems are trained on annotated dialogues, which, in turn, reflect certain domain information (e.g., restaurants or hotels in a given region). However, when such domain knowledge changes (e.g., new restaurants open), the initial dialogue model may become obsolete, decreasing the overall performance of the system. Through a number of experiments, we show, for instance, that adding 50{\%} of new slot-values reduces of about 55{\%} the dialogue state-tracker performance. In light of such evidence, we suggest that automatic adaptation of training dialogues is a valuable option for re-training obsolete models. We experimented with a dialogue adaptation approach based on fine-tuning a generative language model on domain changes, showing that a significant reduction of performance decrease can be obtained."
}