Modeling Hip Hop Challenge-Response Lyrics as Machine Translation

We cast the problem of hip hop lyric generation as a translation problem, automatically learn a machine translation system that accepts hip hop lyric challenges and improvises rhyming responses, and show that improving the training data by learning an unsupervised rhyme detection scheme further improves performance. Our approach using unsupervised induction of stochastic transduction grammars is the first to apply the learning algorithms of SMT to the woefully under-explored genre of lyrics in music. A novel feature of our model is that it is completely unsupervised and does not make use of any a priori linguistic or phonetic information. Unlike the handful of previous approaches to modeling lyrics, we choose the domain of hip hop lyrics which is particularly noisy and unstructured. In order to cope with the noisy nature of the data in this domain, we compare the effect of two data selection schemes on the quality of the responses generated, and show the superiority of selection via a dedicated rhyme scheme detector that is also acquired through unsupervised learning. We also propose two strategies to mitigate the effect of disfluencies in the data which are common in the domain of hip hop lyrics, on the performance of our model. Despite the particularly noisy and unstructured nature of the domain, our model produces fluent and rhyming responses compared to a standard phrase based SMT baseline in human evaluations.