Style transfer has been widely explored in natural language generation with non-parallel corpus by extracting a notion of style from source and target domain corpus . A common aspect among the existing approaches is the prerequisite of joint annotations across all the stylistic dimensions under consideration… Availability of such dataset across a combination of styles is a limiting factor in extending state-of-the art style transfer setups to multiple style dimensions . In our work, we attempt to relax this restriction on requirement of jointly annotated data across multiple styles being inspected and make use of independently acquired data across different style dimensions without any additional annotations . Through quantitative and qualitative evaluation, we show the ability of our model to control for styles across multiple style-dimensions while preserving content of the input text and compare it against baselines which involve cascaded uni-dimensional style transfer models . We show the can of our new model to . use it to preserve content of text and to improve its re-writing capability to multiple styles by employing multiple language models as discriminators

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Keywords : style - multiple - transfer - styles - dimensions -

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