Building a Linguistics based Loss Function for Dialogue Generation
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2020
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Swarthmore College. Dept. of Linguistics
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en
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Full copyright to this work is retained by the student author. It may only be used for non-commercial, research, and educational purposes. All other uses are restricted.
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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.