Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach
Igor Shalyminov, Sung‐Jin Lee, Arash Eshghi, Oliver Lemon · 2019
Learning with minimal data is one of the key challenges in the development of practical, production-ready goal-oriented dialogue systems.In a real-world enterprise setting where dialogue systems are developed rapidly and are expected to work robustly for an evergrowing variety of domains, products, and scenarios, efficient learning from a limited number of examples becomes indispensable.In this paper, we introduce a technique to achieve state-of-the-art dialogue generation performance in a few-shot setup, without using any annotated data.We do this by leveraging background knowledge from a larger, more highly represented dialogue sourcenamely, the MetaLWOz dataset.We evaluate our model on the Stanford Multi-Domain Dialogue Dataset, consisting of human-human goal-oriented dialogues in in-car navigation, appointment scheduling, and weather information domains.We show that our few-shot approach achieves state-of-the art results on that dataset by consistently outperforming the previous best model in terms of BLEU and Entity F1 scores, while being more data-efficient by not requiring any data annotation.