An Empirical Study of Self-Disclosure in Spoken Dialogue Systems
An Empirical Study of Self-Disclosure in Spoken Dialogue Systems
复制标题
口语对话系统中自我表露的实证研究
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
A. Black
中科院分区：
文献类型：
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作者：
Abhilasha Ravichander;A. Black
自我披露是在对话中采用的关键社会策略，以建立关系并增加对话深度。它已经在心理学和语言文献中进行了大量研究，特别是因为它能够诱导接受者自我披露的能力，这种现象称为互惠。但是，我们对与自动对话系统的对话中的自我披露如何表现出一无所知，尤其是因为对话框系统的任何自我披露都是显而易见的。在这项工作中，我们通过分析现实世界用户之间的相互作用和在社交对话的背景下分析真实世界用户之间的相互作用和口语对话框系统进行了大规模的定量分析。我们发现，即使在人机对话框中也出现了互惠的指标，对聊天机器人的影响很大，包括教育，谈判和社交对话。
Self-disclosure is a key social strategy employed in conversation to build relations and increase conversational depth. It has been heavily studied in psychology and linguistic literature, particularly for its ability to induce self-disclosure from the recipient, a phenomena known as reciprocity. However, we know little about how self-disclosure manifests in conversation with automated dialog systems, especially as any self-disclosure on the part of a dialog system is patently disingenuous. In this work, we run a large-scale quantitative analysis on the effect of self-disclosure by analyzing interactions between real-world users and a spoken dialog system in the context of social conversation. We find that indicators of reciprocity occur even in human-machine dialog, with far-reaching implications for chatbots in a variety of domains including education, negotiation and social dialog.