This article introduces Alquist 5.0, our SocialBot that was designed for the Alexa Prize SocialBot Grand Challenge 5. Building upon previous iterations, we present the integration of our novel neural response generator (NRG) Barista within a hybrid architecture that combines traditional predefined dialogues with advanced neural response generation. We provide a comprehensive analysis of the current state-of-the-art NRGs and large language models (LLMs), leveraging these insights to enhance Barista’s capabilities. A key focus of our development was in ensuring the safety of our chatbot and implementing robust measures to prevent profanity and inappropriate content. Additionally, we incorporated a new search engine to improve information retrieval and response accuracy. Expanding the capabilities of our system, we designed Alquist 5.0 to accommodate multimodal devices, utilizing APL templates enriched with custom features to deliver an outstanding conversational experience complemented by an excellent user interface. This paper offers detailed insights into the development of Alquist 5.0, which effectively addresses evolving user demands while preserving its empathetic and knowledgeable conversational prowess across a wide range of topics.

This paper provides a pioneering examination and enhancement of generative chat models, with a specific focus on the BlenderBot 3 model. Through meticulous interaction with a diverse set of human participants, we dissected the fundamental components of these models, unveiling several deficiencies, including long-term memory and entity recognition. Leveraging these insights, we engineered refined, streamlined iterations, culminating in a chatbot that transcends the capabilities of all existing models. Our work follows Occam’s razor principle and proves that, for tasks with relatively low complexity, using large overparameterized models instead of smaller ones does not bring significant benefits but increases latency, which may result in a lowered overall user experience. In upholding our commitment to transparency and the progression of shared knowledge, we have made our improved model universally accessible through open-source distribution.

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.

The intent recognition is an essential algorithm of any conversational AI application. It is responsible for the classification of an input message into meaningful classes. In many bot development platforms, we can configure the NLU pipeline. Several intent recognition services are currently available as an API, or we choose from many open-source alternatives. However, there is no comparison of intent recognition services and open-source algorithms. Many factors make the selection of the right approach to the intent recognition challenging in practice. In this paper, we suggest criteria to choose the best intent recognition algorithm for an application. We present a dataset for evaluation. Finally, we compare selected public NLU services with selected open-source algorithms for intent recognition.