Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues
Yue Feng Yunlong Jiao Animesh Prasad Nikolaos Aletras Emine Yilmaz Gabriella Kazai
User Satisfaction Modeling (USM) is one of the popular choices for
task-oriented dialogue systems evaluation, where user satisfaction typically
depends on whether the user's task goals were fulfilled by the system.
Task-oriented dialogue systems use task schema, which is a set of task
attributes, to encode the user's task goals. Existing studies on USM neglect
explicitly modeling the user's 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 user's preferences
regarding the task attributes are fulfilled by the system for predicting the
user's 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.