Topic
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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16 Mar 2022
TL;DR: This work ran Wizard of Oz studies with 30 participants to evaluate user expectations for text and voice chatbot design variants and identified preferences for intent interpretation and revealed variations in user expectations based on the interface affordances.
Abstract: Chatbots have garnered interest as conversational interfaces for a variety of tasks. While general design guidelines exist for chatbot interfaces, little work explores analytical chatbots that support conversing with data. We explore Gricean Maxims to help inform the basic design of effective conversational interaction. We also draw inspiration from natural language interfaces for data exploration to support ambiguity and intent handling. We ran Wizard of Oz studies with 30 participants to evaluate user expectations for text and voice chatbot design variants. Results identified preferences for intent interpretation and revealed variations in user expectations based on the interface affordances. We subsequently conducted an exploratory analysis of three analytical chatbot systems (text + chart, voice + chart, voice-only) that implement these preferred design variants. Empirical evidence from a second 30-participant study informs implications specific to data-driven conversation such as interpreting intent, data orientation, and establishing trust through appropriate system responses.
15 citations
TL;DR: A comparative study was conducted between the United States and the United Arab Emirates (UAE) to investigate how users in the different cultures perceive the features of chatbot-driven news and how they view ethical issues concerning chatbot journalism as discussed by the authors .
14 citations
TL;DR: It is found that center-embedded sentences are perceived as less humanlike than right-branching sentences and more plausible sentences are regarded as more humanlike, however, the effect of plausibility of the sentence on perceived humanness is smaller for center- embedded sentences than for right- Branchesing sentences.
Abstract: In a Turing test, a judge decides whether their conversation partner is either a machine or human. What cues does the judge use to determine this? In particular, are presumably unique features of human language actually perceived as humanlike? Participants rated the humanness of a set of sentences that were manipulated for grammatical construction: linear right-branching or hierarchical center-embedded and their plausibility with regard to world knowledge. We found that center-embedded sentences are perceived as less humanlike than right-branching sentences and more plausible sentences are regarded as more humanlike. However, the effect of plausibility of the sentence on perceived humanness is smaller for center-embedded sentences than for right-branching sentences. Participants also rated a conversation with either correct or incorrect use of the context by the agent. No effect of context use was found. Also, participants rated a full transcript of either a real human or a real chatbot, and we found that chatbots were reliably perceived as less humanlike than real humans, in line with our expectation. We did, however, find individual differences between chatbots and humans.
14 citations
26 Jul 2019
TL;DR: A data-driven framework for designing customer servicechatbot that utilizes the past customer behavior data from clickstreams and a customer service chatbot is introduced and applied to a cartoon streaming service, Laftel.
Abstract: User experience in customer service is critical. It is because customer service is what a customer first requests for a service. The service fails to satisfactory response will cause a crucial damage. Albeit business includes a chatbot for better responsiveness, customization is still necessary to fulfill the satisfaction from customer service. For customization, a designer performs qualitative research such as surveys, self-reports, interviews, and user observation to pull out key characteristics and to build personas based on the characteristics. However, a small sample size and cognitive limitation of a researcher demand more data to model persona better. Therefore, in this study, we introduce a data-driven framework for designing customer service chatbot that utilizes the past customer behavior data from clickstreams and a customer service chatbot. We apply this framework to a cartoon streaming service, Laftel. In result, we generate three types of customer service chatbots for three personas such as explorer, soft user, and hard user. In the future, we will validate our result by conducting a field experiment.
14 citations
TL;DR: In this article , the authors proposed a method to solve the problem of unstructured data in the context of data augmentation in the field of computer vision. But they did not provide any supporting information.
Abstract: File S1 Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
14 citations