Parody is a figurative device used for mimicking entities for comedic or
critical purposes. Parody is intentionally humorous and often involves sarcasm.
This paper explores jointly modelling these figurative tropes with the goal of
improving performance of political parody detection in tweets. To this end, we
present a multi-encoder model that combines three parallel encoders to enrich
parody-specific representations with humor and sarcasm information. Experiments
on a publicly available data set of political parody tweets demonstrate that
our approach outperforms previous state-of-the-art methods.

本文提出了一个多编码器模型，旨在将幽默和讽刺信息与夸张描绘这些比喻意象相结合，以提高政治恶搞检测的性能；在政治恶搞推文的公开可用数据集上进行的实验表明，我们的方法优于以前的最先进方法。

将幽默和讽刺结合以提高政治模仿检测

Combining Humor and Sarcasm for Improving Political Parody Detection

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。