Data di Pubblicazione:
2021
Abstract:
In recent years conversation has become a key channel for human-computer interaction. Dialogue personalization could result in an important aspect, making sense of users' features when engaged in a conversation with a machine. A feature that has been properly taken into account is the user's mental model, a crucial aspect since it determines users' expectations and the way they interact with a chatbot. In this position paper, we propose a theoretical framework that combines existing meta-mental models (behaviour-based and lexical-based ) in a computational model that can be used to automatically detect the users' mental model from the dialogues with a chatbot by exploiting Linguistic theory and Machine Learning techniques.
Tipologia CRIS:
04A-Conference paper in volume
Keywords:
Conversational agents; Mental model; Neural networks
Elenco autori:
Alloatti F.; Cena F.; Di Caro L.; Ferrod R.; Siragusa G.
Link alla scheda completa:
Titolo del libro:
Joint Proceedings of the ACM IUI 2021 Workshops co-located with 26th ACM Conference on Intelligent User Interfaces (ACM IUI 2021)
Pubblicato in: