Which Factors Predict the Chat Experience of a Natural Language Generation Dialogue Service?


In this paper, we proposed a conceptual model to predict the chat experience in a natural language generation dialog system. We evaluated the model with 120 participants with Partial Least Squares Structural Equation Modeling (PLS-SEM) and obtained an R-square (R2) with 0.541. The model considers various factors, including the prompts used for generation; coherence, sentiment, and similarity in the conversation; and users’ perceived dialog agents’ favorability. We then further explore the effectiveness of the subset of our proposed model. The results showed that users’ favorability and coherence, sentiment, and similarity in the dialogue are positive predictors of users’ chat experience. Moreover, we found users may prefer dialog agents with characteristics of Extroversion, Openness, Conscientiousness, Agreeableness, and Non-Neuroticism. Through our research, an adaptive dialog system might use collected data to infer factors in our model, predict the chat experience for users through these factors, and optimize it by adjusting prompts.

Paper published at *Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA) 🏆
I-Sheng (Eason) Chen
I-Sheng (Eason) Chen
1st-year PhD Student at Human-Computer Interaction Institute

Eason is a first-year PhD student in HCII at CMU.