Style transfer has been widely explored in natural language generation with non-parallel corpus by directly or indirectly 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. While cascading single-dimensional models across multiple styles is a possibility, it suffers from content loss, especially when the style dimensions are not completely independent of each other. 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. We initialize an encoder-decoder setup with large transformer-based language models pre-trained on a generic corpus and enhance its re-writing capability to multiple styles by employing multiple  language models as discriminators. 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 state-of-the-art uni-dimensional style transfer models.
Style transfer has been widely explored in natural language generation with non-parallel corpus by directly or indirectly extracting a notion of style from source and target domain corpus. A common shortcoming of existing approaches is the prerequisite of joint annotations across all the stylistic dimensions under consideration. Availability of such dataset across a combination of styles limits the extension of these setups to multiple style dimensions. While cascading single-dimensional models across multiple styles is a possibility, it suffers from content loss, especially when the style dimensions are not completely independent of each other. In our work, we relax this requirement of jointly annotated data across multiple styles by using independently acquired data across different style dimensions without any additional annotations. We initialize an encoder-decoder setup with transformer-based language model pre-trained on a generic corpus and enhance its re-writing capability to multiple target style dimensions by employing multiple style-aware language models as discriminators. Through quantitative and qualitative evaluation, we show the ability of our model to control styles across multiple style dimensions while preserving content of the input text . We compare it against baselines involving cascaded state-of-the-art uni-dimensional style transfer models.
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