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.

中文翻译：

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Alquist 4.0：使用生成模型和对话个性化实现社交智能

开放域对话系统 Alquist 的目标是进行连贯且引人入胜的对话，这可以被视为社交智能的基准之一。该系统的第四个版本是在 Alexa Prize Socialbot Grand Challenge 4 中开发的，带来了两个主要创新。第一个解决了连贯性，第二个解决了对话的参与度。对于有关连贯性的创新，我们提出了一种结合手工设计的响应和生成模型的新型混合方法。所提出的方法利用手工设计的对话、域外检测和神经响应生成器。手工设计的对话引导用户完成高质量的对话流程。域外检测识别出用户偏离了预定义的流程，并防止系统生成可能对意外用户输入没有意义的脚本化响应。最后，神经响应生成器根据对话的上下文生成响应，该响应对意外的用户输入做出正确的反应，并将对话返回到手工设计的对话的边界。我们提出的参与创新主要受到著名的探索-利用困境的启发。要与对话伙伴进行引人入胜的对话，必须了解他们的偏好和兴趣——探索。此外，为了吸引合作伙伴，我们必须利用我们已经学到的知识——剥削。在这项工作中，