模拟：在电子邮件的智能响应中建模人物角色
Impersonation: Modeling Persona in Smart Responses to Email
摘要：
在本文中，我们提出的设计，实现和有效性产生个性化的建议，电子邮件回复。为了根据用户的风格和个性来个性化电子邮件响应，我们根据用户过去对电子邮件的响应来建模用户角色。该模型被添加到使用所有用户电子邮件的过去响应跨用户创建的基于语言的模型中。用户模型捕捉给定特定上下文的用户的典型响应。上下文包括所接收的电子邮件、电子邮件的接收者以及诸如日历活动、偏好等的其他外部信号。上下文连同用户个性（例如，外向的，正式的，保守的，等等）用于建议响应。这些响应可以是多种模式的混合：电子邮件回复（文本），音频剪辑等。这有助于使响应尽可能地模仿用户，并帮助用户提高生产力，同时保留她在响应中的标记。
英文摘要：
In this paper, we present design, implementation, and effectiveness of generating personalized suggestions for email replies. To personalize email responses based on users style and personality, we model the users persona based on her past responses to emails. This model is added to the language-based model created across users using past responses of the all user emails. A users model captures the typical responses of the user given a particular context. The context includes the email received, recipient of the email, and other external signals such as calendar activities, preferences, etc. The context along with users personality (e.g., extrovert, formal, reserved, etc.) is used to suggest responses. These responses can be a mixture of multiple modes: email replies (textual), audio clips, etc. This helps in making responses mimic the user as much as possible and helps the user to be more productive while retaining her mark in the responses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
