Automatically imitating input text is a common task in natural language generation, often used to create humorous results. Classic algorithms for learning to imitate text, e.g. simple Markov chains, usually have a trade-off between originality and syntactic correctness. We present two ways of automatically parodying philosophical statements from examples overcoming this issue, and show how these can work in interactive systems as well. The first algorithm uses interpolated Markov models with extensions to improve the quality of the generated texts. For the second algorithm, we propose dynamically extracting templates and filling these with new content. To illustrate these algorithms, we implemented TorfsBot, a Twitterbot imitating the witty, semi-philosophical tweets of professor Rik Torfs, the previous KU Leuven rector. We found that users preferred generative models that focused on local coherent sentences, rather than those mimicking the global structure of a philosophical statement. The proposed algorithms are thus valuable new tools for automatic parody as well as template learning systems.

本研究提出两种算法用于自动模仿哲理陈述并克服原创性和句法正确性之间的平衡问题，一种使用插值马尔可夫模型以提高生成文本的质量，另一种则提议动态提取并填充新内容以生成句子模板。这些算法是自动拟仿和模板学习系统的有价值的新工具。

使用插值马尔科夫模型和动态模板生成哲学性语句