{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:56:05Z","timestamp":1760597765766},"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":[[2017,8]]},"abstract":"<jats:p>Capturing the semantic interaction of pairs of words across arguments and proper argument representation are both crucial issues in implicit discourse relation recognition. The current state-of-the-art represents  arguments as distributional vectors that are computed via bi-directional Long Short-Term Memory networks (BiLSTMs), known to have significant model complexity.In contrast, we demonstrate that word-weighted averaging can encode argument representation which  can incorporate word pair information efficiently. By saving an order of magnitude in parameters, our proposed model achieves equivalent performance, but trains seven times faster.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/562","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T05:14:07Z","timestamp":1501218847000},"page":"4026-4032","source":"Crossref","is-referenced-by-count":22,"title":["SWIM: A Simple Word Interaction Model for Implicit Discourse Relation Recognition"],"prefix":"10.24963","author":[{"given":"Wenqiang","family":"Lei","sequence":"first","affiliation":[{"name":"School of Computing, National University of Singapore"}]},{"given":"Xuancong","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research"}]},{"given":"Meichun","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Linguistics and Translation, City University of Hong Kong"}]},{"given":"Ilija","family":"Ilievski","sequence":"additional","affiliation":[{"name":"Graduate School for Integrative Sciences and Engineering, National University of Singapore"}]},{"given":"Xiangnan","family":"He","sequence":"additional","affiliation":[{"name":"School of Computing, National University of Singapore"}]},{"given":"Min-Yen","family":"Kan","sequence":"additional","affiliation":[{"name":"School of Computing, National University of Singapore"}]}],"member":"10584","event":{"number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"acronym":"IJCAI-2017","name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","start":{"date-parts":[[2017,8,19]]},"theme":"Artificial Intelligence","location":"Melbourne, Australia","end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T07:54:32Z","timestamp":1501228472000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/562"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/562","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}