  Considerable progress has been made towards conversational models that
generate coherent and fluent responses by training large language models on
large dialogue datasets. These models have little or no control of the
generated responses and miss two important features: continuous dialogue skills
integration and seamlessly leveraging diverse knowledge sources. In this paper,
we propose the Adapter-Bot, a dialogue model that uses a fixed backbone
conversational model such as DialGPT (Zhang et al., 2019) and triggers
on-demand dialogue skills (e.g., emphatic response, weather information, movie
recommendation) 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.), 8 goal-oriented skills (e.g. weather
information, movie recommendation, etc.), and personalized and emphatic
responses. We evaluate our model using automatic evaluation by comparing it
with existing state-of-the-art conversational models, and we have released an
interactive system at adapter.bot.ust.hk.
