The Adapter-Bot: All-In-One Controllable Conversational Model
Zhaojiang Lin, Andrea Madotto, Yejin Bang, Pascale Fung
Abstract
In this paper, we present the Adapter-Bot, a generative chat-bot that uses a fixed backbone conversational model such as DialGPT (Zhang et al. 2019) and triggers on-demand dialogue skills via different adapters (Houlsby et al. 2019). Each adapter can be trained independently, thus allowing a continual integration of skills without retraining the entire model. Depending on the skills, the model is able to process multiple knowledge types, such as text, tables, and graphs, in a seamless manner. The dialogue skills can be triggered automatically via a dialogue manager, or manually, thus allowing high-level control of the generated responses. At the current stage, we have implemented 12 response styles (e.g., positive, negative etc.), 6 goal-oriented skills (e.g. weather information, movie recommendation, etc.), and personalized and emphatic responses.