使用内插马尔可夫模型和动态模板生成哲学陈述
Generating Philosophical Statements using Interpolated Markov Models and Dynamic Templates
摘要：
自动模仿输入文本是自然语言生成中的一项常见任务，通常用于创建幽默的结果。用于学习模仿文本的经典算法，例如简单的马尔可夫链，通常在原创性和语法正确性之间进行权衡。我们提出了两种方法，自动模仿哲学语句的例子克服了这个问题，并显示如何在交互式系统中工作。第一种算法使用扩展的内插马尔可夫模型来提高生成文本的质量。对于第二种算法，我们提出动态提取模板并填充新内容。为了说明这些算法，我们实现了TorfsBot，这是一个模仿前鲁汶大学校长Rik Torfs教授的诙谐，半哲学的推文的Twitterbot。我们发现，用户更喜欢专注于局部连贯句子的生成模型，而不是那些模仿哲学陈述的全局结构的模型。因此，所提出的算法是有价值的新工具，自动模仿以及模板学习系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
