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Creators/Authors contains: "St. Clair, Jack"

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  1. ; ; (, International Conference on Natural Language Processing)
    Although people have the ability to en- gage in vapid dialogue without effort, this may not be a uniquely human trait. Since the 1960’s researchers have been trying to create agents that can generate artificial conversation. These programs are com- monly known as chatbots. With increasing use of neural networks for dialog genera- tion, some conclude that this goal has been achieved. This research joins the quest by creating a dialog generating Recurrent Neural Network (RNN) and by enhancing the ability of this network with auxiliary loss functions and a beam search. Our cus- tom loss functions achieve better cohesion and coherence by including calculations of Maximum Mutual Information (MMI) and entropy. We demonstrate the effectiveness of this system by using a set of custom evaluation metrics inspired by an abun- dance of previous research and based on tried-and-true principles of Natural Lan- guage Processing. 
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    Accepted Manuscript Full Text Available