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. We establish baseline results on this dataset (about 270K utterances and 30k dialogues) using several state-of-the-art models. Experimental results show that dialogue models that plan over the knowledge graph can make full use of related knowledge to generate more diverse multi-turn conversations.

Introduction. Building a human-like conversational agent is one of long-cherished goals in Artificial Intelligence (AI) (Turing, 2009). Typical conversations involve exchanging information (Zhang et al., 2018), recommending something (Li et al., 2018), and completing tasks (Bordes et al., 2016), most of which rely on background knowledge. However, many dialogue systems only rely on utterances and responses as training data, without explicitly exploiting knowledge associated with them, which sometimes results in uninformative and inappropriate responses (Wang et al., 2018). Although there exist some work that use external background knowledge to generate more informative responses (Liu et al., 2018; Yin et al., 2015; Zhu et al., 2017), these systems usually generate responses to answer questions instead of asking questions or leading the conversation.

Discussion / Conclusion. In this paper, we build a human-like conversational agent by endowing it with the ability of proactively leading the conversation. To achieve this goal, we create a new dataset named DuConv. Each dialog in DuConv is created by two crowdsourced workers, where one acts as the conversation leader and the other acts as the follower. The leader is provided with a knowledge graph and asked to sequentially change the discussed topics following the given conversation goal, and meanwhile, keep the dialogue as natural and engaging as possible. We establish baseline results on DuConv using several state-of-the-art models. Experimental results show that dialogue models that plan over knowledge graph can make more full use of related knowledge to generate more diverse conversations. Our dataset and proposed models are publicly available, which can be used as benchmarks for future research on constructing knowledge-driven proactive dialogue systems.