Rapformer：使用去噪自动编码器的条件说唱歌词生成
Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders
摘要：
结合符号产生语言的能力是人类智能的一个决定性特征，特别是在通过歌词艺术讲故事的背景下。我们开发了一种基于任何文本内容合成说唱诗的方法（例如，新闻文章），或者用于增强预先存在的说唱歌词。我们的方法，称为Rapformer，是基于训练一个基于transformer的去噪自动编码器，从歌词中提取的内容词重建说唱歌词，试图保留基本含义，同时匹配目标风格。Rapformer采用了一种新颖的基于BERT的释义方案来增强押韵，使输出歌词的平均押韵密度增加10%。在三个不同的输入域上的实验结果表明，Rapformer能够生成技术上流畅的诗句，在内容保留和风格转移之间提供良好的权衡。此外，一个类似图灵测试的实验表明，Rapformer在25%的时间里欺骗了人类歌词专家。
英文摘要：
The ability to combine symbols to generate language is a defining characteristic of human intelligence, particularly in the context of artistic story-telling through lyrics. We develop a method for synthesizing a rap verse based on the content of any text (e.g., a news article), or for augmenting pre-existing rap lyrics. Our method, called Rapformer, is based on training a Transformer-based denoising autoencoder to reconstruct rap lyrics from content words extracted from the lyrics, trying to preserve the essential meaning, while matching the target style. Rapformer features a novel BERT-based paraphrasing scheme for rhyme enhancement which increases the average rhyme density of output lyrics by 10%. Experimental results on three diverse input domains show that Rapformer is capable of generating technically fluent verses that offer a good trade-off between content preservation and style transfer. Furthermore, a Turing-test-like experiment reveals that Rapformer fools human lyrics experts 25% of the time.
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。
