DopeLearning：一种说唱歌词生成的计算方法
DopeLearning: A Computational Approach to Rap Lyrics Generation
摘要：
创作说唱歌词既需要创造力来构建一个有意义的，有趣的故事，也需要抒情技巧来产生复杂的押韵模式，这是良好流动的基石。我们提出了一个说唱歌词生成方法，捕捉这两个方面。首先，我们开发了一个预测模型，从一组候选下一行中识别现有歌词的下一行。该模型基于两种机器学习技术：RankSVM算法和具有新颖结构的深度神经网络模型。结果表明，该预测模型可以从随机选择的299条线中识别出真正的下一条线，准确率为17%，即，概率是随机的50倍其次，我们采用预测模型来结合现有歌曲的台词，产生具有韵律和意义的歌词。对所产生的歌词的评估表明，在定量押韵密度方面，该方法比最好的人类说唱歌手高出21%。说唱歌词生成器已被部署为一个名为DeepBeat的在线工具，该工具的性能已通过分析其使用日志进行了评估。该分析表明，机器学习的排名与用户偏好相关。
英文摘要：
Writing rap lyrics requires both creativity to construct a meaningful, interesting story and lyrical skills to produce complex rhyme patterns, which form the cornerstone of good flow. We present a rap lyrics generation method that captures both of these aspects. First, we develop a prediction model to identify the next line of existing lyrics from a set of candidate next lines. This model is based on two machine-learning techniques: the RankSVM algorithm and a deep neural network model with a novel structure. Results show that the prediction model can identify the true next line among 299 randomly selected lines with an accuracy of 17%, i.e., over 50 times more likely than by random. Second, we employ the prediction model to combine lines from existing songs, producing lyrics with rhyme and a meaning. An evaluation of the produced lyrics shows that in terms of quantitative rhyme density, the method outperforms the best human rappers by 21%. The rap lyrics generator has been deployed as an online tool called DeepBeat, and the performance of the tool has been assessed by analyzing its usage logs. This analysis shows that machine-learned rankings correlate with user preferences.
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