Alquist 2.0: Alexa Prize Socialbot Based on Sub-Dialogue Models

Abstract

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.

References in corpus (2)

• Convolutional Neural Networks for Sentence Classification
• Conversational AI: The Science Behind the Alexa Prize

Cited by in corpus (5)

• Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversations
• Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems
• Alquist 4.0: Towards Social Intelligence Using Generative Models and Dialogue Personalization
• Alquist 3.0: Alexa Prize Bot Using Conversational Knowledge Graph
• Building A User-Centric and Content-Driven Socialbot