{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T10:05:43Z","timestamp":1773655543293,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>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.\u00a0\u00a0<\/jats:p>","DOI":"10.24963\/ijcai.2019\/702","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"5053-5059","source":"Crossref","is-referenced-by-count":22,"title":["Adversarial Transfer for  Named Entity Boundary Detection with Pointer Networks"],"prefix":"10.24963","author":[{"given":"Jing","family":"Li","sequence":"first","affiliation":[{"name":"Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates"}]},{"given":"Deheng","family":"Ye","sequence":"additional","affiliation":[{"name":"Tencent AI Lab, Shenzhen, China"}]},{"given":"Shuo","family":"Shang","sequence":"additional","affiliation":[{"name":"Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:51:11Z","timestamp":1564285871000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/702"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/702","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}