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Task-oriented Dialogue (ToD) agents are mostly limited to a few widely-spokenlanguages, mainly due to the high cost of acquiring training data for eachlanguage. Existing low-cost approaches that rely on cross-lingual embeddings ornaive machine translation sacrifice a lot of accuracy for data efficiency, andlargely fail in creating a usable dialogue agent. We propose automatic methodsthat use ToD training data in a source language to build a high-qualityfunctioning 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 priorwork 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 andexisting full-shot methods for ToD agents. We achieve this by (1) improving thedialogue 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. InChinese to English transfer, in the zero-shot setting, our method achieves46.7% 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 targetlanguage is used, we improve the state-of-the-art by 15.2% and 14.0%, comingwithin 5% of full-shot training.

Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation