{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T07:33:07Z","timestamp":1768721587469,"version":"3.49.0"},"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>Document classification is an essential task in many real world applications. Existing approaches adopt both text semantics and document structure to obtain the document representation. However, these models usually require a large collection of annotated training instances, which are not always feasible, especially in low-resource settings. In this paper, we propose a multi-task learning framework to jointly train multiple related document classification tasks. We devise a hierarchical architecture to make use of the shared knowledge from all tasks to enhance the document representation of each task. We further propose an inter-attention approach to improve the task-specific modeling of documents with global information. Experimental results on 15 public datasets demonstrate the benefits of our proposed model.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/495","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"3569-3575","source":"Crossref","is-referenced-by-count":20,"title":["Hierarchical Inter-Attention Network for Document Classification with Multi-Task Learning"],"prefix":"10.24963","author":[{"given":"Bing","family":"Tian","sequence":"first","affiliation":[{"name":"RIIT, TNList, Dept. of Computer Science and Technology, Tsinghua University, Beijing, China"}]},{"given":"Yong","family":"Zhang","sequence":"additional","affiliation":[{"name":"RIIT, TNList, Dept. of Computer Science and Technology, Tsinghua University, Beijing, China"}]},{"given":"Jin","family":"Wang","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of California, Los Angeles"}]},{"given":"Chunxiao","family":"Xing","sequence":"additional","affiliation":[{"name":"RIIT, TNList, Dept. of Computer Science and Technology, Tsinghua University, Beijing, China"}]}],"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":"2019-07-28T07:49:42Z","timestamp":1564300182000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/495"}},"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\/495","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}