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Creative GANs for generating poems, lyrics, and metaphors

2019-09-20 · Asir Saeed, Suzana Ilić, Eva Zangerle

Generative models for text have substantially contributed to tasks like machine translation and language modeling, using maximum likelihood optimization (MLE). However, for creative text generation, where multiple outputs are possible and originality and uniqueness are encouraged, MLE falls short. Methods optimized for MLE lead to outputs that can be generic, repetitive and incoherent. In this work, we use a Generative Adversarial Network framework to alleviate this problem. We evaluate our framework on poetry, lyrics and metaphor datasets, each with widely different characteristics, and report better performance of our objective function over other generative models.

📄 PDF Abstract BibTeX arXiv:1909.09534

Code (1)

Machine-Learning-Tokyo/Poetry-GAN 공식 구현

Tasks

Generative Adversarial NetworkLanguage ModelingLanguage ModellingMachine TranslationText GenerationTranslation

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