Rating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features
This work addresses rating prediction for conversational task assistants, which is incremental as it builds on existing methods by integrating new features.
The paper tackled the problem of predicting user ratings in Conversational Task Assistants by proposing TB-Rater, a Transformer model that combines conversational-flow and user behavior features, achieving improved offline rating prediction on real data from the Alexa TaskBot challenge.
Predicting the success of Conversational Task Assistants (CTA) can be critical to understand user behavior and act accordingly. In this paper, we propose TB-Rater, a Transformer model which combines conversational-flow features with user behavior features for predicting user ratings in a CTA scenario. In particular, we use real human-agent conversations and ratings collected in the Alexa TaskBot challenge, a novel multimodal and multi-turn conversational context. Our results show the advantages of modeling both the conversational-flow and behavioral aspects of the conversation in a single model for offline rating prediction. Additionally, an analysis of the CTA-specific behavioral features brings insights into this setting and can be used to bootstrap future systems.