{"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/context-aware-natural-language-generation","title":"Context-aware Natural Language Generation with Recurrent Neural Networks","arxiv_id":"1611.09900","date":"2016-11-29","proceeding":null,"authors":["Jian Tang","Yifan Yang","Sam Carton","Ming Zhang","Qiaozhu Mei"],"abstract":"This paper studied generating natural languages at particular contexts or\nsituations. We proposed two novel approaches which encode the contexts into a\ncontinuous semantic representation and then decode the semantic representation\ninto text sequences with recurrent neural networks. During decoding, the\ncontext information are attended through a gating mechanism, addressing the\nproblem of long-range dependency caused by lengthy sequences. We evaluate the\neffectiveness of the proposed approaches on user review data, in which rich\ncontexts are available and two informative contexts, sentiments and products,\nare selected for evaluation. Experiments show that the fake reviews generated\nby our approaches are very natural. Results of fake review detection with human\njudges show that more than 50\\% of the fake reviews are misclassified as the\nreal reviews, and more than 90\\% are misclassified by existing state-of-the-art\nfake review detection algorithm.","url_abs":"http://arxiv.org/abs/1611.09900v1","url_pdf":"http://arxiv.org/pdf/1611.09900v1.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":"context-aware-natural-language-generation","repo_url":"https://github.com/chirag2796/RNN-LSTM-for-Text-Generation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.09900","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}