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      Creative GANs for generating poems, lyrics, and metaphors

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          Abstract

          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.

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          Chinese Poetry Generation with Recurrent Neural Networks

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            Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models

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              Word Ordering Without Syntax

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                Author and article information

                Journal
                20 September 2019
                Article
                1909.09534

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                cs.CL cs.LG

                Theoretical computer science, Artificial intelligence

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