Prompt-based or in-context learning has achieved high zero-shot performance on many natural language generation (NLG) tasks. Here we explore the performance of prompt-based learning for simultaneously controlling the personality and the semantic accuracy of an NLG for task-oriented dialogue. We experiment with prompt-based learning on the PERSONAGE restaurant recommendation corpus to generate semantically and stylistically-controlled text for 5 different Big-5 personality types: agreeable, disagreeable, conscientious, unconscientious, and extravert. We test two different classes of discrete prompts to generate utterances for a particular personality style: (1) prompts that demonstrate generating directly from a meaning representation that includes a personality specification; and (2) prompts that rely on first converting the meaning representation to a textual pseudo-reference, and then using the pseudo-reference in a textual style transfer (TST) prompt. In each case, we show that we can vastly improve performance by over-generating outputs and ranking them, testing several ranking functions based on automatic metrics for semantic accuracy, personality-match, and fluency. We also test whether NLG personality demonstrations from the restaurant domain can be used with meaning representations for the video game domain to generate personality stylized utterances about video games. Our findings show that the TST prompts produces the highest semantic accuracy (78.46% for restaurants and 87.6% for video games) and personality accuracy (100% for restaurants and 97% for video games). Our results on transferring personality style to video game utterances are surprisingly good. To our knowledge, there is no previous work testing the application of prompt-based learning to simultaneously controlling both style and semantic accuracy in NLG.

翻译：基于提示或上下文学习已在许多自然语言生成（NLG）任务中实现了高零样本性能。本文探索了提示学习在同时控制任务导向对话中NLG的人格风格和语义准确性方面的表现。我们在PERSONAGE餐厅推荐语料库上实验了提示学习，为5种不同的大五人格类型（宜人型、不宜人型、尽责型、不尽责型、外向型）生成语义和风格受控的文本。我们测试了两类离散提示以生成特定人格风格的语句：（1）直接从包含人格规格的语义表示生成的提示；（2）依赖先将语义表示转换为文本伪参考，然后在文本风格迁移（TST）提示中使用该伪参考的提示。在每种情况下，我们展示了通过过度生成输出并进行排序可大幅提升性能，并基于语义准确性、人格匹配度和流畅性等自动指标测试了多种排序函数。我们还测试了是否可将餐厅领域的NLG人格演示与电子游戏领域的语义表示结合，以生成关于电子游戏的人格风格化语句。研究结果表明，TST提示产生了最高的语义准确性（餐厅领域78.46%，电子游戏领域87.6%）和人格准确性（餐厅领域100%，电子游戏领域97%）。我们将人格风格迁移至电子游戏语句的结果出乎意料地好。据我们所知，尚无前人工作测试过将提示学习应用于同时控制NLG中的风格和语义准确性。