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We propose a simple yet effective approach,\nwhich incorporates auxiliary multi-task and adversarial objectives, for label\nprediction and bag-of-words prediction, respectively. We show, both\nqualitatively and quantitatively, that the style and content are indeed\ndisentangled in the latent space. This disentangled latent representation\nlearning method is applied to style transfer on non-parallel corpora. We\nachieve substantially better results in terms of transfer accuracy, content\npreservation and language fluency, in comparison to previous state-of-the-art\napproaches.","url_abs":"http://arxiv.org/abs/1808.04339v2","url_pdf":"http://arxiv.org/pdf/1808.04339v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"disentangled-representation-learning-for-non","repo_url":"https://github.com/vineetjohn/linguistic-style-transfer","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"disentangled-representation-learning-for-non","repo_url":"https://github.com/sharan21/disentangled-style-transfer-vae","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"disentangled-representation-learning-for-non","repo_url":"https://github.com/h3lio5/linguistic-style-transfer-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"style-transfer","task_name":"Style Transfer"},{"task_slug":"text-style-transfoer","task_name":"Text Style Transfer"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.04339","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.04339"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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