  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.
