Alquist: The Alexa Prize Socialbot

2018/04/18 by Jan Pichl, Petr Marek, Pichl, Jan +9
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Speech and dialogue systems #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1804.06705

openalex publication_date 2018/04/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• A Neural Conversational Model
• How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
• Partially observable Markov decision processes for spoken dialog systems

Related

• Alquist 2.0: Alexa Prize Socialbot Based on Sub-Dialogue Models
• Alquist 3.0: Alexa Prize Bot Using Conversational Knowledge Graph
• Alquist 4.0: Towards Social Intelligence Using Generative Models and Dialogue Personalization
• Alquist 5.0: Dialogue Trees Meet Generative Models. A Novel Approach for Enhancing SocialBot Conversations
• An Ensemble Model with Ranking for Social Dialogue