Proactive Human-Machine Conversation with Explicit Conversation Goals

Though great progress has been made for human-machine conversation, current dialogue system is still in its infancy: it usually converses passively and utters words more as a matter of response, rather than on its own initiatives. In this paper, we take a radical step towards building a human-like conversational agent: endowing it with the ability of proactively leading the conversation (introducing a new topic or maintaining the current topic). To facilitate the development of such conversation systems, we create a new dataset named DuConv where one acts as a conversation leader and the other acts as the follower. The leader is provided with a knowledge graph and asked to sequentially change the discussion topics, following the given conversation goal, and meanwhile keep the dialogue as natural and engaging as possible. DuConv enables a very challenging task as the model needs to both understand dialogue and plan over the given knowledge graph.

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.

3.3 Conversation Goal Assignment Given the knowledge graph, we sample some knowledge paths, which are used as conversation goals. Specifically, we focus on the simple but challenging scenario: naturally shifting the topics twice, i.e., from “[start]” state to “topic a” then finally to “topic b”. We sample two linked entities in our knowledge graph as ‘topic a” and “topic b” to construct the knowledge path. About 30k different knowledge paths are sampled and used as conversation goals for knowledge-driven conversation crowdsourcing, where half of the knowledge paths are from the one-step relation set while the other half are from the two-step relation set.