{"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/hierarchical-attention-transfer-network-for","title":"Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification","arxiv_id":null,"date":"2018-04-26","proceeding":"Thirty-Second AAAI Conference on Artificial Intelligence 2018 4","authors":["Zheng Li","Ying WEI","Yu Zhang","Qiang Yang"],"abstract":"Cross-domain sentiment classification aims to leverage useful information in a source domain to help do sentiment classifi- cation in a target domain that has no or little supervised infor- mation. Existing cross-domain sentiment classification meth- ods cannot automatically capture non-pivots, i.e., the domain- specific sentiment words, and pivots, i.e., the domain-shared sentiment words, simultaneously. In order to solve this prob- lem, we propose a Hierarchical Attention Transfer Network (HATN) for cross-domain sentiment classification. The pro- posed HATN provides a hierarchical attention transfer mech- anism which can transfer attentions for emotions across do- mains by automatically capturing pivots and non-pivots. Be- sides, the hierarchy of the attention mechanism mirrors the hierarchical structure of documents, which can help locate the pivots and non-pivots better. The proposed HATN consists of two hierarchical attention networks, with one named P-net aiming to find the pivots and the other named NP-net align- ing the non-pivots by using the pivots as a bridge. Specif- ically, P-net firstly conducts individual attention learning to provide positive and negative pivots for NP-net. Then, P- net and NP-net conduct joint attention learning such that the HATN can simultaneously capture pivots and non-pivots and realize transferring attentions for emotions across domains. Experiments on the Amazon review dataset demonstrate the effectiveness of HATN.","url_abs":"https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/16873","url_pdf":"https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16873/16149","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":"hierarchical-attention-transfer-network-for","repo_url":"https://github.com/hsqmlzno1/HATN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"cross-domain-text-classification","task_name":"Cross-Domain Text Classification"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}