Text Generation using Generative Adversarial Training
نویسنده
چکیده
Generative models reduce the need of acquiring laborious labeling for the dataset. Text generation techniques can be applied for improving language models, machine translation, summarization, and captioning. This project experiments on different recurrent neural network models to build generative adversarial networks for generating texts from noise. The trained generator is capable of producing sentences with certain level of grammar and logic.
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