Projects

Conversational recommendation systems (CRS) generate recommendations through an interactive process. However, not all CRS approaches use human conversations as their source of interaction data; the majority of prior CRS work simulates interactions by exchanging entity-level information. As a result, claims of prior CRS work do not generalise to real-world settings where conversations take unexpected turns, or where conversational and intent understanding is not perfect. To tackle this challenge, the research community has started to examine holistic CRS , which are trained using conversational data collected from real-world scenarios. We present a comprehensive survey of holistic CRS methods.

In previous zero-shot DST, transferring learning methods are adopted while the unlabelled data in the target domain is ignored. We leverage the unlabelled data in the zero-shot DST by transforming the zero-shot problem into a few-shot problem with a two-step training strategy. Our proposed methods outperform the baseline by 8%