  RubyStar is a dialog system designed to create "human-like" conversation by
combining different response generation strategies. RubyStar conducts a
non-task-oriented conversation on general topics by using an ensemble of
rule-based, retrieval-based and generative methods. Topic detection, engagement
monitoring, and context tracking are used for managing interaction. Predictable
elements of conversation, such as the bot's backstory and simple question
answering are handled by separate modules. We describe a rating scheme we
developed for evaluating response generation. We find that character-level RNN
is an effective generation model for general responses, with proper parameter
settings; however other kinds of conversation topics might benefit from using
other models.
