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.

提出了一种上下文感知的对话响应重新排序系统，使用神经词嵌入模型和手工或逻辑回归模型，将响应在与当前对话上下文的匹配分数和候选人的概率分布的基础上重新排名，提高了最近提出的端到端任务导向对话系统在具有语音识别错误的实际对话中的性能。