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Chatbot

About: Chatbot is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 24372 citations. The topic is also known as: IM bot & AI chatbot.


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TL;DR: In this article, the authors introduce a cost-efficient and robust evaluation framework that replaces human-bot conversations with conversations between bots, where human judges only annotate for each entity in a conversation whether they think it is human or not.
Abstract: The lack of time-efficient and reliable evaluation methods hamper the development of conversational dialogue systems (chatbots). Evaluations requiring humans to converse with chatbots are time and cost-intensive, put high cognitive demands on the human judges, and yield low-quality results. In this work, we introduce \emph{Spot The Bot}, a cost-efficient and robust evaluation framework that replaces human-bot conversations with conversations between bots. Human judges then only annotate for each entity in a conversation whether they think it is human or not (assuming there are humans participants in these conversations). These annotations then allow us to rank chatbots regarding their ability to mimic the conversational behavior of humans. Since we expect that all bots are eventually recognized as such, we incorporate a metric that measures which chatbot can uphold human-like behavior the longest, i.e., \emph{Survival Analysis}. This metric has the ability to correlate a bot's performance to certain of its characteristics (e.g., \ fluency or sensibleness), yielding interpretable results. The comparably low cost of our framework allows for frequent evaluations of chatbots during their evaluation cycle. We empirically validate our claims by applying \emph{Spot The Bot} to three domains, evaluating several state-of-the-art chatbots, and drawing comparisons to related work. The framework is released as a ready-to-use tool.

22 citations

Journal ArticleDOI
TL;DR: In this paper, an intelligent knowledge-based conversational agent system architecture is proposed to support customer services in e-commerce sales and marketing, and a prototype system is built in a real-world setting.

22 citations

Book ChapterDOI
01 Jan 2019
TL;DR: There are use cases and scope where it could be easier, quicker, and cheaper to use readily available online intent classification and conversation management frameworks to build your chatbot client.
Abstract: In the previous chapter, we discussed how to build an in-house chatbot framework with natural language and conversation capabilities. Building a solution from scratch has advantages that we discussed previously. However, there are use cases and scope where it could be easier, quicker, and cheaper to use readily available online intent classification and conversation management frameworks to build your chatbot client.

21 citations

Book ChapterDOI
24 Oct 2018
TL;DR: It is found that personality has a significant positive effect on the user experience of chatbot interfaces, but this effect is dependent on context, the job it performs, and its user group.
Abstract: In this study, we investigated the impact of a match in personality between a chatbot and the user. Previous research have proposed that personality can offer a stable pattern to how chatbots are perceived, and add consistency to the user experience. The assumptions regarding the effects of personality was investigated by measuring the effects of two chatbot agents, with two levels of personality, on the user experience. This study found that personality has a significant positive effect on the user experience of chatbot interfaces, but this effect is dependent on context, the job it performs, and its user group.

21 citations

Journal ArticleDOI
14 Oct 2020
TL;DR: Three chatbot designs for emotion management for distributed teams are presented and design implications are highlighted and chatbot design recommendations for enhancing emotion management in teams are discussed.
Abstract: Fueled by the pervasion of tools like Slack or Microsoft Teams, the usage of text-based communication in distributed teams has grown massively in organizations. This brings distributed teams many advantages, however, a critical shortcoming in these setups is the decreased ability of perceiving, understanding and regulating emotions. This is problematic because better team members? abilities of emotion management positively impact team-level outcomes like team cohesion and team performance, while poor abilities diminish communication flow and well-being. Leveraging chatbot technology in distributed teams has been recognized as a promising approach to reintroduce and improve upon these abilities. In this article we present three chatbot designs for emotion management for distributed teams. In order to develop these designs, we conducted three participatory design workshops which resulted in 153 sketches. Subsequently, we evaluated the designs following an exploratory evaluation with 27 participants. Results show general stimulating effects on emotion awareness and communication efficiency. Further, they report emotion regulation and increased compromise facilitation through social and interactive design features, but also perceived threats like loss of control. With some design features adversely impacting emotion management, we highlight design implications and discuss chatbot design recommendations for enhancing emotion management in teams.

21 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023916
20221,413
2021564
2020617
2019528
2018326