  Dialog response ranking is used to rank response candidates by considering
their relation to the dialog history. Although researchers have addressed this
concept for open-domain dialogs, little attention has been focused on
task-oriented dialogs. Furthermore, no previous studies have analyzed whether
response ranking can improve the performance of existing dialog systems in real
human-computer dialogs with speech recognition errors. In this paper, we
propose a context-aware dialog response re-ranking system. Our system reranks
responses in two steps: (1) it calculates matching scores for each candidate
response and the current dialog context; (2) it combines the matching scores
and a probability distribution of the candidates from an existing dialog system
for response re-ranking. By using neural word embedding-based models and
handcrafted or logistic regression-based ensemble models, we have improved the
performance of a recently proposed end-to-end task-oriented dialog system on
real dialogs with speech recognition errors.
