  Dialogue models are able to generate coherent and fluent responses, but they
can still be challenging to control and may produce non-engaging, unsafe
results. This unpredictability diminishes user trust and can hinder the use of
the models in the real world. To address this, we introduce DialGuide, a novel
framework for controlling dialogue model behavior using natural language rules,
or guidelines. These guidelines provide information about the context they are
applicable to and what should be included in the response, allowing the models
to generate responses that are more closely aligned with the developer's
expectations and intent. We evaluate DialGuide on three tasks in open-domain
dialogue response generation: guideline selection, response generation, and
response entailment verification. Our dataset contains 10,737 positive and
15,467 negative dialogue context-response-guideline triplets across two domains
- chit-chat and safety. We provide baseline models for the tasks and benchmark
their performance. We also demonstrate that DialGuide is effective in the
dialogue safety domain, producing safe and engaging responses that follow
developer guidelines.
