{"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/sentiinc-incorporating-sentiment-information","title":"SentiInc: Incorporating Sentiment Information into Sentiment Transfer Without Parallel Data","arxiv_id":null,"date":"2020-04-08","proceeding":"European Conference on Information Retrieval 2020 4","authors":["Kartikey Pant","Yash Verma","Radhika Mamidi"],"abstract":"Sentiment-to-sentiment transfer involves changing the sentiment of the given text while preserving the underlying information. In this work, we present a model SentiInc for sentiment-to-sentiment transfer using unpaired mono-sentiment data. Existing sentiment-to-sentiment transfer models ignore the valuable sentiment-specific details already present in the text. We address this issue by providing a simple framework for encoding sentiment-specific information in the target sentence while preserving the content information. This is done by incorporating sentiment based loss in the back-translation based style transfer. Extensive experiments over the Yelp dataset show that the SentiInc outperforms state-of-the-art methods by a margin of as large as equation ~11% in G-score. The results also demonstrate that our model produces sentiment-accurate and information-preserved sentences.","url_abs":"https://link.springer.com/chapter/10.1007/978-3-030-45442-5_39#Abs1","url_pdf":"https://link.springer.com/content/pdf/10.1007%2F978-3-030-45442-5_39.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":[],"tasks":[{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"style-transfer","task_name":"Style Transfer"},{"task_slug":"text-generation","task_name":"Text Generation"},{"task_slug":"text-style-transfoer","task_name":"Text Style Transfer"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-style-transfer-on-yelp-review-dataset-1","task":"Text Style Transfer","dataset":"Yelp Review Dataset (Large)","model":"SentiInc","rank_in_archive_order":1,"of":2,"metrics":{"G-Score (BLEU, Accuracy)":"59.17"},"uses_additional_data":false},{"leaderboard":"/sota/text-style-transfer-on-yelp-review-dataset","task":"Text Style Transfer","dataset":"Yelp Review Dataset (Small)","model":"SentiInc","rank_in_archive_order":3,"of":8,"metrics":{"G-Score (BLEU, Accuracy)":"66.25"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}