Expressions of Style in Information Seeking Conversation with an Agent

Paul Thomas, Daniel J. McDuff, M. Czerwinski, Nick Craswell
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引用次数: 14

Abstract

Past work in information-seeking conversation has demonstrated that people exhibit different conversational styles---for example, in word choice or prosody---that differences in style lead to poorer conversations, and that partners actively align their styles over time. One might assume that this would also be true for conversations with an artificial agent such as Cortana, Siri, or Alexa; and that agents should therefore track and mimic a user's style. We examine this hypothesis with reference to a lab study, where 24 participants carried out relatively long information-seeking tasks with an embodied conversational agent. The agent combined topical language models with a conversational dialogue engine, style recognition and alignment modules. We see that "style'' can be measured in human-to-agent conversation, although it looks somewhat different to style in human-to-human conversation and does not correlate with self-reported preferences. There is evidence that people align their style to the agent, and that conversations run more smoothly if the agent detects, and aligns to, the human's style as well.
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与代理人信息寻求对话中的风格表达
过去在信息寻求对话方面的研究表明,人们表现出不同的对话风格——例如,在用词或韵律方面——风格的差异会导致更糟糕的对话,而且随着时间的推移,合作伙伴会主动调整他们的风格。有人可能会认为,这也适用于与人工智能(如Cortana、Siri或Alexa)的对话;因此,代理应该跟踪和模仿用户的风格。我们通过一项实验室研究来检验这一假设,在这项研究中,24名参与者通过一个具体化的对话代理执行了相对较长的信息搜索任务。该代理将主题语言模型与会话对话引擎、风格识别和对齐模块相结合。我们看到,“风格”可以在人与人之间的对话中测量,尽管它看起来与人与人之间的对话风格有些不同,并且与自我报告的偏好无关。有证据表明,人们会将自己的风格与智能体保持一致,如果智能体也检测到并与人类的风格保持一致,那么对话就会进行得更顺利。
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