We present a novel model, Freestyle, that learns to improvise rhyming and fluent responses upon being challenged with a line of hip hop lyrics, by combining both bottomup token based rule induction and top-down rule segmentation strategies to learn a stochastic transduction grammar that simultaneously learns both phrasing and rhyming associations.In this attack on the woefully under-explored natural language genre of music lyrics, we exploit a strictly unsupervised transduction grammar induction approach.Our task is particularly ambitious in that no use of any a priori linguistic or phonetic information is allowed, even though the domain of hip hop lyrics is particularly noisy and unstructured.We evaluate the performance of the learned model against a model learned only using the more conventional bottom-up token based rule induction, and demonstrate the superiority of our combined token based and rule segmentation induction method toward generating higher quality improvised responses, measured on fluency and rhyming criteria as judged by human evaluators.To highlight some of the inherent challenges in adapting other algorithms to this novel task, we also compare the quality of the responses generated by our model to those generated by an out-ofthe-box phrase based SMT system.We tackle the challenge of selecting appropriate training data for our task via a dedicated rhyme scheme detection module, which is also acquired via unsupervised learning and report improved quality of the generated responses.Finally, we report results with Maghrebi French hip hop lyrics indicating that our model performs surprisingly well with no special adaptation to other languages.

We attack an inexplicably under-explored language genre of spoken language-lyrics in music-via completely unsupervised induction of an SMT-style stochastic transduction grammar for hip hop lyrics, yielding a fully-automatically learned challenge-response system that produces rhyming lyrics given an input. Unlike previous efforts, we choose the domain of hip hop lyrics, which is particularly unstructured and noisy. A novel feature of our approach is that it is completely unsupervised and requires no a priori linguistic or phonetic knowledge. In spite of the level of difficulty of the challenge, the model nevertheless produces fluent output as judged by human evaluators, and performs significantly better than widely used phrase-based SMT models upon the same task.

We introduce a new type of transduction grammar that allows for learning of probabilistic phrasal bilexica, leading to a significant improvement in spoken language translation accuracy. The current state-of-the-art in statistical machine translation relies on a complicated and crude pipeline to learn probabilistic phrasal bilexica—the very core of any speech translation system. In this paper, we present a more principled approach to learning probabilistic phrasal bilexica, based on stochastic transduction grammar learning applicable to speech corpora. Index Terms: speech translation, transduction theory, lexicon extraction