{"type":"ScholarlyArticle","title":"Adversarial Transfer for Named Entity Boundary Detection with Pointer Networks","authors":[{"name":"Jing Li","slug":"jing-li","given":"Jing","family":"Li","affiliation":"Inception Institute of Artificial Intelligence , Abu Dhabi , United Arab Emirates"},{"name":"Deheng Ye","slug":"deheng-ye","given":"Deheng","family":"Ye","affiliation":"Tencent AI Lab , Shenzhen , China"},{"name":"Shuo Shang","slug":"shuo-shang","given":"Shuo","family":"Shang","affiliation":"Inception Institute of Artificial Intelligence , Abu Dhabi , United Arab Emirates"}],"abstract":"In this paper, we focus on named entity boundary detection, which aims to detect the start and end boundaries of an entity mention in text, without predicting its type. A more accurate and robust detection approach is desired to alleviate error propagation in downstream applications, such as entity linking and fine-grained typing systems. Here, we first develop a novel entity boundary labeling approach with pointer networks, where the output dictionary size depends on the input, which is variable. Furthermore, we propose AT-Bdry, which incorporates adversarial transfer learning into an end-to-end sequence labeling model to encourage domain-invariant representations. More importantly, AT-Bdry can reduce domain difference in data distributions between the source and target domains, via an unsupervised transfer learning approach (i.e., no annotated target-domain data is necessary). We conduct Formal Text to Formal Text, Formal Text to Informal Text and ablation evaluations on five benchmark datasets. Experimental results show that AT-Bdry achieves state-of-the-art transferring performance against recent baselines.","doi":"10.24963/ijcai.2019/702","url":"https://ijcai.org/proceedings/2019/702","pdf":"https://ijcai.org/proceedings/2019/0702.pdf","markdown":"https://ijcai.org/proceedings/2019/702.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/702","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Natural Language Processing","track":"Main track","number":702,"pages":{"first":5053,"last":5059},"keywords":["Natural Language Processing: Named Entities","Natural Language Processing: Natural Language Processing"],"videos":[],"references":[{"position":1,"raw":"[Abhishek et al., 2017] Abhishek Abhishek, Ashish Anand, and Amit Awekar. 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