Proactive Human-Machine Conversation with Explicit Conversation Goals
Table 1: Overview of the conversation dataset DuConv.
['# dialogs', '29858']
In this section, we describe the creation of DuConv in details. It contains four steps: knowledge crawling, knowledge graph construction, conversation goal assignment, and conversation crowdsourcing. We limit the dialogue topics in DuConv to movies and film stars, and crawl this related knowledge from the internet. Then we build our knowledge graph with these crawled data. After constructing our knowledge graph, we randomly sample two linked entities to construct the conversation goal, denoted as “[start] → topic_a → topic_b”, and ask two annotators to conduct knowledge-driven conversations, with one playing as the conversation leader and the other one playing as the follower. The leader needs to change the conversation topics following the conversation goal and meanwhile keep the conversation as engaging as possible. All those conversations are recorded and around 30k conversations are finally used in DuConv after filtering dirty/offensive parts.