Abstract
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. The baseline systems along
with the dataset are publicly available