# Alquist 3.0: Alexa Prize Bot Using Conversational Knowledge Graph

arXiv: https://arxiv.org/abs/2011.03261 · HTML: https://mlsanity.com/paper/2011.03261/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Alquist 4.0: Towards Social Intelligence Using Generative Models and Dialogue Personalization](https://mlsanity.com/paper/2109.07968/) (2021)
- [A Transformer-based Response Evaluator for Open-Domain Spoken Conversation](https://mlsanity.com/paper/2302.04424/) (2023)
- [Text Summarization of Czech News Articles Using Named Entities](https://mlsanity.com/paper/2104.10454/) (2021)
- [Detecting early signs of depression in the conversational domain: The role of transfer learning in low-resource scenarios](https://mlsanity.com/paper/2204.10841/) (2022)
- [Metric Learning and Adaptive Boundary for Out-of-Domain Detection](https://mlsanity.com/paper/2204.10849/) (2022)

## Abstract (arXiv)

The third version of the open-domain dialogue system Alquist developed within the Alexa Prize 2020 competition is designed to conduct coherent and engaging conversations on popular topics. The main novel contribution is the introduction of a system leveraging an innovative approach based on a conversational knowledge graph and adjacency pairs. The conversational knowledge graph allows the system to utilize knowledge expressed during the dialogue in consequent turns and across conversations. Dialogue adjacency pairs divide the conversation into small conversational structures, which can be combined and allow the system to react to a wide range of user inputs flexibly. We discuss and describe Alquist's pipeline, data acquisition and processing, dialogue manager, NLG, knowledge aggregation, and a hierarchy of adjacency pairs. We present the experimental results of the individual parts of the system.