The use of persona-grounded retrieval-based chatbots is crucial for personalized conversations, but there are several challenges that need to be addressed. 1) In general, collecting persona-grounded corpus is very expensive. 2) The chatbot system does not always respond in consideration of persona at real applications. To address these challenges, we propose a plug-and-play persona prompting method. Our system can function as a standard open-domain chatbot if persona information is not available. We demonstrate that this approach performs well in the zero-shot setting, which reduces the dependence on persona-ground training data. This makes it easier to expand the system to other languages without the need to build a persona-grounded corpus. Additionally, our model can be fine-tuned for even better performance. In our experiments, the zero-shot model improved the standard model by 7.71 and 1.04 points in the original persona and revised persona, respectively. The fine-tuned model improved the previous state-of-the-art system by 1.95 and 3.39 points in the original persona and revised persona, respectively. To the best of our knowledge, this is the first attempt to solve the problem of personalized response selection using prompt sequences. Our code is available on github~\footnote{https://github.com/rungjoo/plug-and-play-prompt-persona}.

翻译：基于人物特征的检索式聊天机器人对于个性化对话至关重要，但仍存在若干待解决的挑战：1）收集人物特征语料库通常成本高昂；2）实际应用中聊天机器人系统并非总能依据人物特征进行响应。针对这些挑战，我们提出一种即插即用式人物提示方法。当无人物特征信息可用时，本系统可充当标准开放域聊天机器人。实验证明该方法在零样本场景下表现优异，显著降低了对人物特征训练数据的依赖，使得系统无需构建人物特征语料库即可便捷扩展至其他语言。此外，我们的模型可通过微调获得更优性能。在实验中，零样本模型在原始人物特征和修正人物特征上分别较标准模型提升7.71和1.04分；微调模型在原始人物特征和修正人物特征上分别超过此前最优系统1.95和3.39分。据我们所知，这是首次尝试利用提示序列解决个性化回复选择问题。相关代码已开源发布于github~\footnote{https://github.com/rungjoo/plug-and-play-prompt-persona}。