Rating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features

• URL: http://arxiv.org/abs/2309.11307v1
• Date: Wed, 20 Sep 2023 13:34:03 GMT
• Title: Rating Prediction in Conversational Task Assistants with Behavioral and Conversational-Flow Features
• Authors: Rafael Ferreira, David Semedo and Jo\~ao Magalh\~aes
• Abstract summary: We propose TB-Rater, a Transformer model which combines conversational-flow features with user behavior features for predicting user ratings. In particular, we use real human-agent conversations and ratings collected in the Alexa TaskBot challenge. 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.
• Score: 6.188306785668896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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