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Deep Learning for Creative Language Understanding

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Creative language—the sort found in novels, film, and comics—contains a wide range of linguistic phenomena, from increased syntactic complexity (e.g., metaphors, sarcasm) to high-level discourse structures such as narrative and character arcs. In this talk, I explore how we can use deep learning to understand, generate, and answer questions about creative language. In particular, I present neural architectures for two different tasks involving creative language understanding: 1) modeling dynamic fictional relationships in novels and 2) predicting dialogue and artwork from comic book panels. I also propose a method to disentangle an author's writing style from the content of their words by strategically weakening parts of the network architecture. These tasks are motivated through quiz bowl, a trivia game that contains many questions about novels, paintings, and comics, for which I present deep models that are competitive with human players. I conclude by discussing future plans to build more engaging conversational agents by leveraging systems for creative language understanding and question answering.