Alquist 2.0: Alexa Prize Socialbot Based on Sub-Dialogue Models
This work addresses the problem of building more natural and varied conversational AI for users, though it appears incremental as an update to a previous system.
The paper tackles the challenge of creating engaging social dialogue systems by introducing Alquist 2.0, which uses ontology-based topic nodes with LSTM-based sub-dialogue models, resulting in a system that can trigger personalized interactions during each session.
This paper presents the second version of the dialogue system named Alquist competing in Amazon Alexa Prize 2018. We introduce a system leveraging ontology-based topic structure called topic nodes. Each of the nodes consists of several sub-dialogues, and each sub-dialogue has its own LSTM-based model for dialogue management. The sub-dialogues can be triggered according to the topic hierarchy or a user intent which allows the bot to create a unique experience during each session.