Giuseppe Gallipoli
Text Style Transfer: a Cycle-Consistent Adversarial Approach.
Rel. Luca Cagliero, Moreno La Quatra. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2022
Abstract
Following the recent advancements in Natural Language Processing, an increasing number of tasks are gaining importance and new downstream applications are being designed. As an example, in the field of content moderation, it would be desirable to have a system capable of transforming comments written using offensive language into non-offensive versions whereas, considering intelligent writing assistants, their purpose is to help users to improve the quality of their writings. Both use cases are two possible subtasks of Text Style Transfer (TST), whose objective is to convert an input sentence carrying a certain source style (e.g., offensive) into its counterpart in the desired target attribute (e.g., non-offensive).
The main challenge is represented by content preservation, which requires to rewrite the input text in the target style without modifying its original meaning
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