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        <title>How Positive Are You: Text Style Transfer using Adaptive Style Embedding</title>
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        <namePart type="given">Heejin</namePart>
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            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
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            <namePart type="given">Donia</namePart>
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            <namePart type="given">Chengqing</namePart>
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    <abstract>The prevalent approach for unsupervised text style transfer is disentanglement between content and style. However, it is difficult to completely separate style information from the content. Other approaches allow the latent text representation to contain style and the target style to affect the generated output more than the latent representation does. In both approaches, however, it is impossible to adjust the strength of the style in the generated output. Moreover, those previous approaches typically perform both the sentence reconstruction and style control tasks in a single model, which complicates the overall architecture. In this paper, we address these issues by separating the model into a sentence reconstruction module and a style module. We use the Transformer-based autoencoder model for sentence reconstruction and the adaptive style embedding is learned directly in the style module. Because of this separation, each module can better focus on its own task. Moreover, we can vary the style strength of the generated sentence by changing the style of the embedding expression. Therefore, our approach not only controls the strength of the style, but also simplifies the model architecture. Experimental results show that our approach achieves better style transfer performance and content preservation than previous approaches.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.191</identifier>
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%0 Conference Proceedings
%T How Positive Are You: Text Style Transfer using Adaptive Style Embedding
%A Kim, Heejin
%A Sohn, Kyung-Ah
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F kim-sohn-2020-positive
%X The prevalent approach for unsupervised text style transfer is disentanglement between content and style. However, it is difficult to completely separate style information from the content. Other approaches allow the latent text representation to contain style and the target style to affect the generated output more than the latent representation does. In both approaches, however, it is impossible to adjust the strength of the style in the generated output. Moreover, those previous approaches typically perform both the sentence reconstruction and style control tasks in a single model, which complicates the overall architecture. In this paper, we address these issues by separating the model into a sentence reconstruction module and a style module. We use the Transformer-based autoencoder model for sentence reconstruction and the adaptive style embedding is learned directly in the style module. Because of this separation, each module can better focus on its own task. Moreover, we can vary the style strength of the generated sentence by changing the style of the embedding expression. Therefore, our approach not only controls the strength of the style, but also simplifies the model architecture. Experimental results show that our approach achieves better style transfer performance and content preservation than previous approaches.
%R 10.18653/v1/2020.coling-main.191
%U https://aclanthology.org/2020.coling-main.191
%U https://doi.org/10.18653/v1/2020.coling-main.191
%P 2115-2125

Markdown (Informal)

[How Positive Are You: Text Style Transfer using Adaptive Style Embedding](https://aclanthology.org/2020.coling-main.191) (Kim & Sohn, COLING 2020)

• How Positive Are You: Text Style Transfer using Adaptive Style Embedding (Kim & Sohn, COLING 2020)

ACL

• Heejin Kim and Kyung-Ah Sohn. 2020. How Positive Are You: Text Style Transfer using Adaptive Style Embedding. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2115–2125, Barcelona, Spain (Online). International Committee on Computational Linguistics.