  Different people have different habits of describing their intents in
conversations. Some people tend to deliberate their intents in several
successive utterances, i.e., they use several consistent messages for
readability instead of a long sentence to express their question. This creates
a predicament faced by the application of dialogue systems, especially in
real-world industry scenarios, in which the dialogue system is unsure whether
it should answer the query of user immediately or wait for further
supplementary input. Motivated by such an interesting predicament, we define a
novel Wait-or-Answer task for dialogue systems. We shed light on a new research
topic about how the dialogue system can be more intelligent to behave in this
Wait-or-Answer quandary. Further, we propose a predictive approach named
Predict-then-Decide (PTD) to tackle this Wait-or-Answer task. More
specifically, we take advantage of a decision model to help the dialogue system
decide whether to wait or answer. The decision of decision model is made with
the assistance of two ancillary prediction models: a user prediction and an
agent prediction. The user prediction model tries to predict what the user
would supplement and uses its prediction to persuade the decision model that
the user has some information to add, so the dialogue system should wait. The
agent prediction model tries to predict the answer of the dialogue system and
convince the decision model that it is a superior choice to answer the query of
user immediately since the input of user has come to an end. We conduct our
experiments on two real-life scenarios and three public datasets. Experimental
results on five datasets show our proposed PTD approach significantly
outperforms the existing models in solving this Wait-or-Answer problem.

Computation and Language