Proceedings

Learning to Rap Battle with Bilingual Recursive Neural Networks

Dekai Wu, Karteek Addanki

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Artificial Intelligence and the Arts. Pages 2524-2529.

Abstract

We describe an unconventional line of attack in our quest to teach machines how to rap battle by improvising hip hop lyrics on the fly, in which a novel recursive bilingual neural network, TRAAM, implicitly learns soft, context-dependent generalizations over the structural relationships between associated parts of challenge and response raps, while avoiding the exponential complexity costs that symbolic models would require. TRAAM learns 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, where TRAAM is trained to improvise fluent and rhyming responses to challenge lyrics. The soft structural relationships learned by our TRAAM model are used to improve the probabilistic responses generated by our improvisational response component.

Dekai Wu, and Karteek Addanki. 2015. Learning to Rap Battle with Bilingual Recursive Neural Networks. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 2524–2529. https://ijcai.org/proceedings/2015/358
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