Alquist: The Alexa Prize Socialbot

Abstract

This paper describes a new open domain dialogue system Alquist developed as part of the Alexa Prize competition for the Amazon Echo line of products. The Alquist dialogue system is designed to conduct a coherent and engaging conversation on popular topics. We are presenting a hybrid system combining several machine learning and rule based approaches. We discuss and describe the Alquist pipeline, data acquisition, and processing, dialogue manager, NLG, knowledge aggregation and hierarchy of sub-dialogs. We present some of the experimental results.

References in corpus (3)

• Sequence to Sequence Learning with Neural Networks
• A Neural Conversational Model
• How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation

Cited by in corpus (10)

• Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize
• Challenges in Building Intelligent Open-domain Dialog Systems
• Gunrock 2.0: A User Adaptive Social Conversational System
• Profile Consistency Identification for Open-domain Dialogue Agents
• Alquist 2.0: Alexa Prize Socialbot Based on Sub-Dialogue Models
• Alquist 4.0: Towards Social Intelligence Using Generative Models and Dialogue Personalization
• Alquist 3.0: Alexa Prize Bot Using Conversational Knowledge Graph
• Do We Need Online NLU Tools?
• Generate, Filter, and Rank: Grammaticality Classification for Production-Ready NLG Systems
• Tartan: A retrieval-based socialbot powered by a dynamic finite-state machine architecture