HyLECA: A Framework for Developing Hybrid Long-term Engaging Controlled Conversational Agents

We present HyLECA, an open-source framework designed for the development of long-term engaging controlled conversational agents. HyLECA’s dialogue manager employs a hybrid architecture, combining rule-based methods for controlled dialogue flows with retrieval-based and generation-based approaches to enhance the utterance variability and flexibility. The motivation behind HyLECA lies in enhancing user engagement and enjoyment in task-oriented chatbots by leveraging the natural language generation capabilities of open-domain large language models within the confines of predetermined dialogue flows. Moreover, we discuss the technical capabilities, potential applications, relevance, and adaptability of the system. Lastly, we report preliminary findings from integrating state-of-the-art large language models in simulating a conversation centred on smoking cessation.