With growing societal acceptance and increasing cost efficiency due to mass production, service robots are beginning to cross from the industrial to the social domain. Currently, customer service robots tend to be digital and emulate social interactions through on-screen text, but state-of-the-art research points towards physical robots soon providing customer service in person. This article explores two possibilities. Firstly, whether transfer learning can aid in the improvement of customer service chatbots between business domains. Secondly, the implementation of a framework for physical robots for in-person interaction. Modelled on social interaction with customer support Twitter accounts, transformer-based chatbot models are initially tasked to learn one domain from an initial random weight distribution. Given shared vocabulary, each model is then tasked with learning another domain by transferring knowledge from the prior. Following studies on 19 different businesses, results show that the majority of models are improved when transferring weights from at least one other domain, in particular those that are more data-scarce than others. General language transfer learning occurs, as well as higher-level transfer of similar domain knowledge in several cases. The chatbots are finally implemented on Temi and Pepper robots, with feasibility issues encountered and solutions are proposed to overcome them.
翻译:随着社会接受程度的提高和由于大规模生产而成本效率的提高,服务机器人开始从工业进入社会领域。目前,客户服务机器人往往通过屏幕文字进行数字化和模仿社会互动。客户服务机器人倾向于通过数字化,并通过屏幕文字进行模拟社会互动,但最先进的研究点很快会到物理机器人提供客户服务。本文章探讨了两种可能性:第一,转让学习是否有助于改善商业领域之间的客户服务聊天机;第二,实施物理机器人框架,进行面对面互动;在与客户支持推特账户的社会互动方面进行建模,基于变压器的聊天机模型最初的任务是从最初的随机权重分配中学习一个域。根据共同的词汇,每个模型然后通过转让以前的知识来学习另一个域。在对19个不同企业进行研究之后,结果显示,在至少从另一个领域转让权重时,尤其是从数据偏差的域的域权重时,大多数模型都得到了改进。一般语言转移学习,以及在某些情况下将类似的域知识进行更高层次的转让。在Temi和Bepper机器人上最后提出了克服了它们的可行性问题。