Building a Linguistics based Loss Function for Dialogue Generation

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2020
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Tri-College (Bryn Mawr, Haverford, and Swarthmore Colleges). Department of Linguistics
Haverford College. Department of Computer Science
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Thesis
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eng
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Tri-College users only
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Abstract
This paper will investigate different loss functions used for various natural language processing (NLP) machine learning tasks. These loss functions have proven their worth in the area of machine translation but they have been shown to be inadequate for the task of dialogue generation. Thus, this paper proposes some potential additions to these loss functions that add more linguistic information with the goal of improving dialogue generation to get machine learning algorithms closer to creating human like dialogue.
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