  The open domain-dialogue system Alquist has a goal to conduct a coherent and
engaging conversation that can be considered as one of the benchmarks of social
intelligence. The fourth version of the system, developed within the Alexa
Prize Socialbot Grand Challenge 4, brings two main innovations. The first
addresses coherence, and the second addresses the engagingness of the
conversation. For innovations regarding coherence, we propose a novel hybrid
approach combining hand-designed responses and a generative model. The proposed
approach utilizes hand-designed dialogues, out-of-domain detection, and a
neural response generator. Hand-designed dialogues walk the user through
high-quality conversational flows. The out-of-domain detection recognizes that
the user diverges from the predefined flow and prevents the system from
producing a scripted response that might not make sense for unexpected user
input. Finally, the neural response generator generates a response based on the
context of the dialogue that correctly reacts to the unexpected user input and
returns the dialogue to the boundaries of hand-designed dialogues. The
innovations for engagement that we propose are mostly inspired by the famous
exploration-exploitation dilemma. To conduct an engaging conversation with the
dialogue partners, one has to learn their preferences and interests --
exploration. Moreover, to engage the partner, we have to utilize the knowledge
we have already learned -- exploitation. In this work, we present the
principles and inner workings of individual components of the open-domain
dialogue system Alquist developed within the Alexa Prize Socialbot Grand
Challenge 4 and the experiments we have conducted to evaluate them.
