Fair Transfer of Multiple Style Attributes in Text

• URL: http://arxiv.org/abs/2001.06693v1
• Date: Sat, 18 Jan 2020 15:38:04 GMT
• Title: Fair Transfer of Multiple Style Attributes in Text
• Authors: Karan Dabas, Nishtha Madan, Vijay Arya, Sameep Mehta, Gautam Singh, Tanmoy Chakraborty
• Abstract summary: We show that the transfer of multiple styles cannot be achieved by sequentially performing multiple single-style transfers. We propose a neural network architecture for fairly transferring multiple style attributes in a given text.
• Score: 26.964711594103566
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To preserve anonymity and obfuscate their identity on online platforms users may morph their text and portray themselves as a different gender or demographic. Similarly, a chatbot may need to customize its communication style to improve engagement with its audience. This manner of changing the style of written text has gained significant attention in recent years. Yet these past research works largely cater to the transfer of single style attributes. The disadvantage of focusing on a single style alone is that this often results in target text where other existing style attributes behave unpredictably or are unfairly dominated by the new style. To counteract this behavior, it would be nice to have a style transfer mechanism that can transfer or control multiple styles simultaneously and fairly. Through such an approach, one could obtain obfuscated or written text incorporated with a desired degree of multiple soft styles such as female-quality, politeness, or formalness. In this work, we demonstrate that the transfer of multiple styles cannot be achieved by sequentially performing multiple single-style transfers. This is because each single style-transfer step often reverses or dominates over the style incorporated by a previous transfer step. We then propose a neural network architecture for fairly transferring multiple style attributes in a given text. We test our architecture on the Yelp data set to demonstrate our superior performance as compared to existing one-style transfer steps performed in a sequence.

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