Talk to Me on My Level – Linguistic Alignment for Chatbots
Laura Spillner, Nina Wenig
Abstract
Digital companions and conversational agents are becoming increasingly popular in our everyday lives. Natural language interfaces play an important role in ubiquitous computing: voice assistants are used to control smart home devices and smartphone applications; chatbots serve as an interface to solve tasks and acquire information easily. However, misunderstandings due to non-standard language, expressions that serve social functions without conveying information, or a lack of situational awareness still pose problems for these interfaces. Humans are able to prevent or repair communication failures by imitating their conversation partner’s lexical choices, sentence structures, and overall language style; a mechanism known as Linguistic Alignment. In this paper we present different strategies to easily integrate an alignment effect in natural language interfaces. We implemented a chatbot that imitates alignment and tested it in an online user study with 75 participants. Our results show that alignment helps to decrease user frustration and perceived task workload.