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#MeToo Alexa: How Conversational Systems Respond to Sexual Harassment

机译:#MeToo Alexa:会话系统如何应对性骚扰

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摘要

Conversational AI systems, such as Amazon's Alexa, are rapidly developing from purely transactional systems to social chatbots, which can respond to a wide variety of user requests. In this article, we establish how current state-of-the-art conversational systems react to inappropriate requests, such as bullying and sexual harassment on the part of the user, by collecting and analysing the novel #MeTooAlexa corpus. Our results show that commercial systems mainly avoid answering, while rule-based chatbots show a variety of behaviours and often deflect. Data-driven systems, on the other hand, are often non-coherent, but also run the risk of being interpreted as flirtatious and sometimes react with counter-aggression. This includes our own system, trained on "clean" data, which suggests that inappropriate system behaviour is not caused by data bias.
机译:会话式AI系统(例如Amazon的Alexa)正在从纯粹的事务处理系统迅速发展为可以响应各种用户请求的社交聊天机器人。在本文中,我们通过收集和分析新颖的#MeTooAlexa语料库,来确定当前最先进的会话系统如何应对不适当的请求,例如用户方面的欺凌和性骚扰。我们的结果表明,商业系统主要避免回答,而基于规则的聊天机器人则表现出各种各样的行为,并且经常会出现偏向。另一方面,数据驱动系统通常是不连贯的,但也冒着被解释为轻浮的风险,有时还会产生反攻。这包括我们自己的系统,该系统接受过“干净”数据的培训,这表明不适当的系统行为不是由数据偏差引起的。

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