{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-sentiment-memories-for-sentiment","title":"Learning Sentiment Memories for Sentiment Modification without Parallel Data","arxiv_id":"1808.07311","date":"2018-08-22","proceeding":"EMNLP 2018 10","authors":["Yi Zhang","Jingjing Xu","Pengcheng Yang","Xu sun"],"abstract":"The task of sentiment modification requires reversing the sentiment of the\ninput and preserving the sentiment-independent content. However, aligned\nsentences with the same content but different sentiments are usually\nunavailable. Due to the lack of such parallel data, it is hard to extract\nsentiment independent content and reverse the sentiment in an unsupervised way.\nPrevious work usually can not reconcile sentiment transformation and content\npreservation. In this paper, motivated by the fact the non-emotional context\n(e.g., \"staff\") provides strong cues for the occurrence of emotional words\n(e.g., \"friendly\"), we propose a novel method that automatically extracts\nappropriate sentiment information from learned sentiment memories according to\nspecific context. Experiments show that our method substantially improves the\ncontent preservation degree and achieves the state-of-the-art performance.","url_abs":"http://arxiv.org/abs/1808.07311v1","url_pdf":"http://arxiv.org/pdf/1808.07311v1.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":"learning-sentiment-memories-for-sentiment","repo_url":"https://github.com/lancopku/SMAE","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"text-style-transfoer","task_name":"Text Style Transfer"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.07311","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}