Learning to rap battle with bilingual recursive neural networks
Dekai Wu, Karteek Addanki · Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2015
We describe an unconventional line of attack in our quest to teach machines how to rap battle by improvising lyrics on the fly, in which a novel recursive bilingual neural network implicitly learns soft, context-sensitive generalizations over the structural relationships between associated parts of challenge and response raps, while avoiding the exponential complexity costs that symbolic models would require. Our recursive bilingual neural network learns the feature vectors simultaneously using context from both the challenge and the response such that challenge-response association patterns with similar structure tend to have similar vectors. Improvisation is modeled as a quasi-translation learning problem and our recursive bilingual neural network is trained to improvise fluent and rhyming responses to hip hop lyrical challenges. The soft structural relationships learned by our recursive bilingual neural network are used to improve the probabilistic responses generated by our improvisational response component.