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Reinforcement Learning as an Approach for Language Generation

Natural language generation is a process in which an algorithm tries to generate human-understandable text. It’s a difficult task for artificial intelligence to solve and has recently become a highly explored space. In this paper, we try two approaches to language generation the (1) LSTM model and a (2) GAN network model. We achieved a result of about 45% accuracy with the LSTM model and were able to score 22/28 with the reinforcement learning approach in an environment of our own creation.