@inproceedings{moradshahi-etal-2023-zero,
    title = "Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation",
    author = "Moradshahi, Mehrad  and
      Semnani, Sina  and
      Lam, Monica",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2023.eacl-main.62/",
    doi = "10.18653/v1/2023.eacl-main.62",
    pages = "886--901",
    abstract = "Task-oriented Dialogue (ToD) agents are mostly limited to a few widely-spoken languages, mainly due to the high cost of acquiring training data for each language. Existing low-cost approaches that rely on cross-lingual embeddings or naive machine translation sacrifice a lot of accuracy for data efficiency, and largely fail in creating a usable dialogue agent. We propose automatic methods that use ToD training data in a source language to build a high-quality functioning dialogue agent in another target language that has no training data (i.e. zero-shot) or a small training set (i.e. few-shot). Unlike most prior work in cross-lingual ToD that only focuses on Dialogue State Tracking (DST), we build an end-to-end agent. We show that our approach closes the accuracy gap between few-shot and existing full-shot methods for ToD agents. We achieve this by (1) improving the dialogue data representation, (2) improving entity-aware machine translation, and (3) automatic filtering of noisy translations. We evaluate our approach on the recent bilingual dialogue dataset BiToD.In Chinese to English transfer, in the zero-shot setting, our method achieves 46.7{\%} and 22.0{\%} in Task Success Rate (TSR) and Dialogue Success Rate (DSR) respectively. In the few-shot setting where 10{\%} of the data in the target language is used, we improve the state-of-the-art by 15.2{\%} and 14.0{\%}, coming within 5{\%} of full-shot training."
}