User-adaptive Tourist Information Dialogue System with Yes/No Classifier and Sentiment Estimator.

In this article, we propose a novel approach to evaluate user satisfaction with dialogue systems. Our method combines the strengths of two existing techniques: rule-based utterance selection and language model (LLM)-based utterance generation. By leveraging BERT, a powerful natural language processing tool, we create a yes/no classifier and sentiment estimator that can accurately determine the user’s state transition and sightseeing plan.
To begin, let’s consider the challenge of evaluating user satisfaction with dialogue systems. Traditionally, researchers have relied on explicit ratings or surveys to gauge user satisfaction. However, these methods are limited in their ability to capture the nuances of user experience. Our proposed approach addresses this problem by analyzing the user’s utterances and determining their sentiment in real-time.
Our system consists of two primary components: a yes/no classifier and a sentiment estimator. The classifier is trained on a dataset of user utterances to recognize transitional states, such as whether the user is interested or bored with the dialogue. The sentiment estimator, also based on BERT, assesses the user’s overall sentiment towards the system.
Now, let’s consider how our method improves upon existing approaches. By combining rule-based utterance selection with LLM-generated responses, we can provide more accurate and contextually relevant replies to the user. This hybrid approach ensures that the system can handle unexpected utterances while maintaining a high level of quality in its generated responses.
In addition, our use of BERT enables us to train a robust yes/no classifier and sentiment estimator that are tailored to individual users based on their age. By selecting the appropriate model for each user, we significantly improve the accuracy of our evaluations.
To illustrate the effectiveness of our approach, we conducted experiments using a dataset of user utterances. Our results show that the combination of rule-based selection and LLM-generated responses leads to improved evaluations of user satisfaction compared to using either method alone. Furthermore, our use of BERT-based sentiment analysis demonstrates a significant improvement in accuracy over traditional methods.
In conclusion, our proposed approach represents a major step forward in evaluating user satisfaction with dialogue systems. By leveraging the strengths of rule-based utterance selection and LLM-generated responses, we can provide more accurate and contextually relevant replies to users. Our use of BERT also enables us to train robust sentiment analyzers tailored to individual users, leading to improved evaluations of user satisfaction. As dialogue systems become increasingly ubiquitous, this work has significant implications for improving the overall user experience.