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Towards modeling user behavior in interactions mediated through an automated bidirectional speech translation system

机译：在通过自动双向语音翻译系统介导的交互中建模用户行为

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摘要

This paper addresses modeling user behavior in interactions between two people who do not share a common spoken language and communicate with the aid of an automated bidirectional speech translation system. These interaction settings are complex. The translation machine attempts to bridge the language gap by mediating the verbal communication, noting however that the technology may not be always perfect. In a step toward understanding user behavior in this mediated communication scenario, usability data from doctor-patient dialogs involving a two way English-Persian speech translation system are analyzed. We specifically consider user behavior in light of potential uncertainty in the communication between the interlocutors. We analyze the Retry (Repeat and Rephrase) versus Accept behaviors in the mediated verbal channel and as a result identify three user types - Accommodating, Normal and Picky, and propose a dynamic Bayesian network model of user behavior. To validate the model, we performed offline and online experiments. The experimental results using offline data show that correct user type is clearly identified as a user keeps his/her consistent behavior in a given interaction condition. In the online experiment, agent feedback was presented to users according to the user types. We show high user satisfaction and interaction efficiency in the analysis of user interview, video data, questionnaire and log data.
机译：本文介绍了在不共享通用口头语言的两个人之间的交互中，如何在自动双向语音翻译系统的帮助下进行通信来对用户行为进行建模。这些交互设置很复杂。翻译机尝试通过调解口头交流来弥合语言鸿沟，但是请注意该技术可能并不总是完美的。在理解这种介导的通信场景中的用户行为的步骤中，分析了来自医患对话的可用性数据，该对话涉及双向英语-波斯语语音翻译系统。我们特别考虑到对话者之间沟通中潜在的不确定性，考虑用户的行为。我们分析了介导的口头渠道中的重试（重复和重述）与接受行为，从而确定了三种用户类型-适应，正常和挑剔，并提出了一种动态的用户行为贝叶斯网络模型。为了验证模型，我们进行了离线和在线实验。使用离线数据的实验结果表明，正确识别了用户类型，因为用户在给定的交互条件下保持其一致的行为。在在线实验中，根据用户类型向用户提供了代理反馈。我们在分析用户访谈，视频数据，问卷和日志数据时显示出很高的用户满意度和互动效率。

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