There has been an increased focus on creating conversational open-domain dialogue systems in the spoken dialogue community. Unlike traditional dialogue systems, these conversational systems cannot assume any specific information need or domain restrictions, i.e., the only inherent goal is to converse with the user on an unknown set of topics. While massive improvements in Natural Language Understanding (NLU) and the growth of available knowledge resources can partially support a robust conversation, these conversations generally lack the rapport between two humans that know each other. We developed a robust open-domain conversational system, Athena, that real Amazon Echo users access and evaluate at scale in the context of the Alexa Prize competition. We experiment with methods intended to increase intimacy between Athena and the user by heuristically developing a rule-based user model that personalizes both the current and subsequent conversations and evaluating specific personal opinion question strategies in A/B studies. Our results show a statistically significant positive impact on perceived conversation quality and length when employing these strategies.

翻译：语音对话社区越来越关注创建开放域对话系统。与传统对话系统不同，这些对话系统不能假设任何特定的信息需求或领域限制，即其唯一固有目标是与用户就未知话题集合进行对话。尽管自然语言理解（NLU）的巨大进步和可用知识资源的增长能部分支持稳健的对话，但这些对话通常缺乏相互了解的两个人之间的融洽关系。我们开发了一个稳健的开放域对话系统Athena，在Alexa Prize竞赛背景下，真实Amazon Echo用户能够大规模访问和评估该系统。我们通过启发式方法开发基于规则的用户模型，对当前和后续对话进行个性化处理，并在A/B测试中评估特定个人观点提问策略，实验了旨在提升Athena与用户之间亲密度的多种方法。结果表明，采用这些策略时，感知对话质量和长度均有统计显著的积极影响。