This paper focuses on the computer side of human-computer interaction through natural language, which is the domain of natural language generation (NLG) studies. From a given (usually non-linguistic) input, NLG systems will in principle generate the same fixed text as an output and in order to attain more natural or human-like interaction will often resort to a wide range of strategies for stylistic variation. Among these, the use of computational models of human personality has emerged as a popular alternative in the field and will be the focus of the present work as well. More specifically, the present study describes two machine learning experiments to establish possible relations between personality and content selection (as opposed to the more well-documented relation between personality and surface realisation), and it is, to the best of our knowledge, the first of its kind to address this issue at both macro and micro planning levels, which may arguably pave the way for the future development of more robust personality-dependent systems of this kind.

中文翻译：

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自然语言生成系统中依赖个性的内容选择

本文着重于通过自然语言进行人机交互的计算机端，这是自然语言生成（NLG）研究的领域。从给定的（通常是非语言的）输入中，NLG 系统原则上将生成与输出相同的固定文本，并且为了获得更自然或更人性化的交互，通常会诉诸广泛的文体变化策略。其中，人类性格计算模型的使用已成为该领域的流行替代方案，也将成为当前工作的重点。更具体地说，本研究描述了两个机器学习实验，以建立个性和内容选择之间可能的关系（而不是个性和表面实现之间更有据可查的关系），并且它是，