  We investigate the impact of search strategies in neural dialogue modeling.
We first compare two standard search algorithms, greedy and beam search, as
well as our newly proposed iterative beam search which produces a more diverse
set of candidate responses. We evaluate these strategies in realistic full
conversations with humans and propose a model-based Bayesian calibration to
address annotator bias. These conversations are analyzed using two automatic
metrics: log-probabilities assigned by the model and utterance diversity. Our
experiments reveal that better search algorithms lead to higher rated
conversations. However, finding the optimal selection mechanism to choose from
a more diverse set of candidates is still an open question.

Computation and Language;Machine Learning