  Previous approaches to turn-taking and response generation in conversational
systems have treated it as a two-stage process: First, the end of a turn is
detected (based on conversation history), then the system generates an
appropriate response. Humans, however, do not take the turn just because it is
likely, but also consider whether what they want to say fits the position. In
this paper, we present a model (an extension of TurnGPT) that conditions the
end-of-turn prediction on both conversation history and what the next speaker
wants to say. We found that our model consistently outperforms the baseline
model in a variety of metrics. The improvement is most prominent in two
scenarios where turn predictions can be ambiguous solely from the conversation
history: 1) when the current utterance contains a statement followed by a
question; 2) when the end of the current utterance semantically matches the
response. Treating the turn-prediction and response-ranking as a one-stage
process, our findings suggest that our model can be used as an incremental
response ranker, which can be applied in various settings.
