Retrieval-based and generation-based dialog
Task-oriented, chitchat or open-domain dialogue
Document- and knowledge-grounded dialogue
Conversational IR and recommendation
Humans interact naturally through multi-turn dialogues. Most current systems for information and knowledge access such as search engines are designed for one-shot interactions. This limited form of interaction is not fully natural, and limits the ability for users to express precisely what is needed. In contrast, in human interactions, questions become more and more precise along with the turns of dialogue.
Under this theme, we will study conversation-based human-system interactions in both task-oriented and open-domain applications contexts. In addition to merely generating a response that seems natural as in chitchat, we will perform document- and knowledge-grounded dialogue, so that the dialogue contains useful substance for the user and follows the general or domain-specific knowledge as the user may expect. Natural dialogues are also rich and heterogeneous in terms of intents: one may mix up chitchat with a search intent, a buying intent or a need for mental health healing. The detection of the underlying intent will be crucial in such a context, allowing us to trigger the appropriate process to build the response. Such a dialogue system can be used in multiple application contexts: conversational search, multi-turn question answering, conversational recommendation, and chatbot with emotional goal.