Research Area:  Machine Learning

Abstract:

User Satisfaction Modeling (USM) is one of the popular choices for task-oriented dialogue systems evaluation, where user satisfaction typically depends on whether the users task goals were fulfilled by the system. Task-oriented dialogue systems use task schema, which is a set of task attributes, to encode the users task goals. Existing studies on USM neglect explicitly modeling the users task goals fulfillment using the task schema. In this paper, we propose SG-USM, a novel schema-guided user satisfaction modeling framework. It explicitly models the degree to which the users preferences regarding the task attributes are fulfilled by the system for predicting the users satisfaction level. SG-USM employs a pre-trained language model for encoding dialogue context and task attributes. Further, it employs a fulfillment representation layer for learning how many task attributes have been fulfilled in the dialogue, an importance predictor component for calculating the importance of task attributes. Finally, it predicts the user satisfaction based on task attribute fulfillment and task attribute importance. Experimental results on benchmark datasets (i.e. MWOZ, SGD, ReDial, and JDDC) show that SG-USM consistently outperforms competitive existing methods. Our extensive analysis demonstrates that SG-USM can improve the interpretability of user satisfaction modeling, has good scalability as it can effectively deal with unseen tasks and can also effectively work in low-resource settings by leveraging unlabeled data.

Keywords:  
User Satisfaction Modeling
Task-oriented dialogue systems
Dialogue systems evaluation
SG-USM

Author(s) Name:  Yue Feng, Yunlong Jiao, Animesh Prasad, Nikolaos Aletras, Emine Yilmaz, Gabriella Kazai

Publisher name:  arXiv:2305.16798

DOI:  10.48550/arXiv.2305.16798

Volume Information:  

Paper Link:   https://arxiv.org/abs/2305.16798