Annotating Errors and Emotions in Human-Chatbot Interactions in Italian
This paper describes a novel annotation scheme specifically designed for a customer-service context where written interactions take place between a given user and the chatbot of an Italian telecommunication company. More specifically, the scheme aims to detect and highlight two aspects: the presence of errors in the conversation on both sides (i.e. customer and chatbot) and the “emotional load” of the conversation. This can be inferred from the presence of emotions of some kind (especially negative ones) in the customer messages, and from the possible empathic responses provided by the agent. The dataset annotated according to this scheme is currently used to develop the prototype of a rule-based Natural Language Generation system aimed at improving the chatbot responses and the customer experience overall.
Code (0)
등록된 구현이 없습니다.
Tasks
ChatbotText GenerationSimilar Papers 제목 키워드 기반
LGBTQ-AI? Exploring Expressions of Gender and Sexual Orientation in Chatbots
Chatbots are popular machine partners for task-oriented and social interactions. Human-human computer-mediated communication research has explored how people express their gender and sexuality in online social interactio…
Empathetic Conversational Agents: Utilizing Neural and Physiological Signals for Enhanced Empathetic Interactions
Conversational agents (CAs) are revolutionizing human-computer interaction by evolving from text-based chatbots to empathetic digital humans (DHs) capable of rich emotional expressions. This paper explores the integratio…
Emotion RecognitionInvestigating person-specific errors in chat-oriented dialogue systems
Creating chatbots to behave like real people is important in terms of believability. Errors in general chatbots and chatbots that follow a rough persona have been studied, but those in chatbots that behave like real peop…
ChatbotSymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations
In this paper, we present our participation to the EmoContext shared task on detecting emotions in English textual conversations between a human and a chatbot. We propose four neural systems and combine them to further i…
ChatbotEmotion ClassificationEmotion Recognition in ConversationGeneral ClassificationThe complexities of patient-centred conversational artificial intelligence
Consumer-facing health chatbots powered by large language models (LLMs) are increasingly used for symptom assessment. However, chatbot development and evaluation often rely on cooperative, articulate, simulated patients.…