{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,24]],"date-time":"2023-09-24T04:39:30Z","timestamp":1695530370502},"reference-count":39,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2019,12,1]]},"DOI":"10.1587\/transinf.2019edp7087","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T16:24:02Z","timestamp":1575303842000},"page":"2587-2594","source":"Crossref","is-referenced-by-count":1,"title":["Adversarial Domain Adaptation Network for Semantic Role Classification"],"prefix":"10.1587","volume":"E102.D","author":[{"given":"Haitong","family":"YANG","sequence":"first","affiliation":[{"name":"School of Computing, China Central Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyou","family":"ZHOU","sequence":"additional","affiliation":[{"name":"School of Computing, China Central Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingting","family":"HE","sequence":"additional","affiliation":[{"name":"School of Computing, China Central Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maoxi","family":"LI","sequence":"additional","affiliation":[{"name":"School of Computer Information Engineering, Jiangxi Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] S. Narayanan and S. Harabagiu, \u201cQuestion Answering Based on Semantic Structures,\u201d Proc. 20th International Conference on Computational Linguistics, Switzerland, Article no.693, Aug. 2004. 10.3115\/1220355.1220455","DOI":"10.3115\/1220355.1220455"},{"key":"2","unstructured":"[2] J. Christensen, Mausam, S. Soderland, and O. Etzioni, \u201cSemantic role labeling for open information extraction,\u201d Proc. NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading, Los Angeles, California, pp.52-60, June 2010."},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] M. Surdeanu, S. Harabagiu, J. Williams, and P. Aarseth, \u201cUsing Predicate-argument Structures for Information Extraction,\u201d Proc. 41th Annual Meeting on Association for Computational Linguistics, Sapporo, Japan, pp.8-15, July 2003. 10.3115\/1075096.1075098","DOI":"10.3115\/1075096.1075098"},{"key":"4","unstructured":"[4] D. Liu and D. Gildea, \u201cSemantic role features for machine translation,\u201d Proc. 23th International Conference on Computational Linguistics, Beijing, China, pp.716-724, Aug. 2010."},{"key":"5","unstructured":"[5] D. Wu and P. Fung, \u201cCan semantic role labeling improve SMT?,\u201d Proc. 13th Annual Conference of European Association for Machine Translation, Barcelona, pp.218-225, May 2009."},{"key":"6","unstructured":"[6] D. Xiong, M. Zhang, and H. Li, \u201cModeling the translation of predicate-argument structure for SMT,\u201d Proc. 50th Annual Meeting of the Association for Computational Linguistics, Jeju, Republic of Korea, pp.902-911, July 2012."},{"key":"7","unstructured":"[7] F. Zhai, J. Zhang, Y. Zhou, and C. Zong, \u201cMachine translation by modeling predicate argument structure transformation,\u201d Proc. 23th International Conference on Computational Linguistics, Mumbai, pp.3019-3036, Dec. 2012."},{"key":"8","unstructured":"[8] F. Zhai, J. Zhang, Y. Zhou, and C. Zong, \u201cHandling ambiguities of bilingual predicate argument structures for statistical machine translation,\u201d Proc. 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria, pp.1127-1136, Aug. 2013."},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] J. Blitzer, R. McDonald, and F. Pereira, \u201cDomain Adaptation with Sturctural Correspondance Learning,\u201d Proc. 2006 Conference on Empirical Methods in Natural Language Processing, Sydney, Australia, pp.120-128, July 2006. 10.3115\/1610075.1610094","DOI":"10.3115\/1610075.1610094"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] S.J. Pan, X. Ni, J.-T. Sun, Q. Yang, and Z. Chen, \u201cCross-domain sentiment classification via spectral feature alignment,\u201d Proc. 19th International World Wide Web Conference, Raleigh, North Carolina, USA, pp.751-760, April 2010. 10.1145\/1772690.1772767","DOI":"10.1145\/1772690.1772767"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] J. Yu and J. Jiang, \u201cLearning sentence embeddings with auxiliary tasks for cross-domain sentiment classification,\u201d Proc. 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas, pp.236-246, Nov. 2016. 10.18653\/v1\/d16-1023","DOI":"10.18653\/v1\/D16-1023"},{"key":"12","unstructured":"[12] Y. Kim, K. Stratos, and R. Sarikaya, \u201cFrustratingly easy neural domain adaptation,\u201d Proc. 26th International Conference on Computational Linguistics, Osaka, Japan, pp.387-396, Dec. 2016."},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] H. Yang, T. Zhuang, and C. Zong, \u201cDomain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks,\u201d Transactions of the Association for Computational Linguistics, vol.3, pp.271-282, May 2015. 10.1162\/tacl_a_00138","DOI":"10.1162\/tacl_a_00138"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] B. David, J. Blitzer, C. Koby, and F. Pereira, \u201cAnalysis of representations for domain adaptation,\u201d Proc. 19th International Conference on Neural Information Processing Systems, Canada, pp.137-144, Dec. 2006.","DOI":"10.7551\/mitpress\/7503.003.0022"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] D. Gildea and D. Jurafsky, \u201cAutomatic labeling for semantic roles,\u201d Comput. Linguist., vol.28, no.3, pp.245-288, 2002. 10.1162\/089120102760275983","DOI":"10.1162\/089120102760275983"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] S. Pradhan, K. Hacioglu, V. Krugler, W. Ward, J.H. Martin, and D. Jurafsky, \u201cSupport Vector Learning for Semantic Argument Classification,\u201d Mach. Learn., vol.60, no.1-3, pp.11-39, Sept. 2005. 10.1007\/s10994-005-0912-2","DOI":"10.1007\/s10994-005-0912-2"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] H. Zhao and C. Kit, \u201cParsing Syntactic and Semantic Dependencies with Two Single-Stage Maximum Entropy Models,\u201d Proc. Twelfth Conference on Computational Natural Language Learning, Manchester, England, pp.203-207, Aug. 2008. 10.3115\/1596324.1596360","DOI":"10.3115\/1596324.1596360"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] H. Zhao, W. Chen, J. Kazama, K. Uchimoto, and K. Torisawa, \u201cMultilingual Dependency Learning: Exploiting Rich Features for Tagging Syntactic and Semantic Dependencies,\u201d Proc. Thirteenth Conference on Computational Natural Language Learning, Boulder, Colorado, pp.61-66, June 2009. 10.3115\/1596409.1596419","DOI":"10.3115\/1596409.1596419"},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] J. Li, G. Zhou, H. Zhao, Q. Zhu, and P. Qian, \u201cImproving nominal SRL in Chinese language with verbal SRL information and automatic predicate recognition,\u201d Proc. 2009 Conference on Empirical methods in Natural Language Processing, Singapore, pp.1280-1288, Aug. 2009. 10.3115\/1699648.1699674","DOI":"10.3115\/1699648.1699674"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] N. Xue, \u201cLabeling Chinese Predicates with Semantic Roles,\u201d Comput. Linguist, vol.34, no.2, pp.225-255, June 2008. 10.1162\/coli.2008.34.2.225","DOI":"10.1162\/coli.2008.34.2.225"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] V. Punyakanok, D. Roth, W.-T. Yih, and D. Zimak, \u201cSemantic Role Labeling via Integer Linear Programming Inference,\u201d Proc. 20th International Conference on Computational Linguistics, Switzerland, Article no.1346, Aug. 2004. 10.3115\/1220355.1220552","DOI":"10.3115\/1220355.1220552"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] K. Toutanova, A. Haghighi, and C. Manning, \u201cJoint Learning Improves Semantic Role Labeling,\u201d Proc. 43th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Ann Arbor, Michigan, pp.589-596, June 2005. 10.3115\/1219840.1219913","DOI":"10.3115\/1219840.1219913"},{"key":"23","unstructured":"[23] R. Collobert and J. Weston, \u201cFast semantic extraction using a novel neural network architecture,\u201d Proc. 45th Annual Meeting of the Association of Computational Linguistics, Prague, Czech Republic, pp.560-567, June 2007."},{"key":"24","unstructured":"[24] R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, \u201cNatural language processing (almost) from scratch,\u201d J. Mach. Learn. Res., vol.12, pp.2493-2537, 2011."},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] J. Zhou and W. Xu, \u201cEnd-to-end learning of semantic role labeling using recurrent neural networks,\u201d Proc. 53rd Annual Meeting of the Association for Computational Linguistics, Beijing, China, pp.1127-1137, July 2015. 10.3115\/v1\/p15-1109","DOI":"10.3115\/v1\/P15-1109"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] D. Marcheggiani and I. Titov, \u201cEncoding Sentences with Graph Convolutional Networks for Semantic Role Labeling,\u201d Proc. 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, pp.1506-1515, Sept. 2017. 10.18653\/v1\/d17-1159","DOI":"10.18653\/v1\/D17-1159"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] M. Roth and M. Lapata, \u201cNeural Semantic Role Labeling with Dependency Path Embeddings\u201d Proc. 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, pp.1192-1202, Aug. 2016. 10.18653\/v1\/p16-1113","DOI":"10.18653\/v1\/P16-1113"},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] Z. Li, S. He, J. Cai, Z. Zhang, H. Zhao, G. Liu, L. Li, and L. Si, \u201cA Unified Syntax-aware Framework for Semantic Role Labeling,\u201d Proc. 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, pp.2401-2411, Oct. 2018. 10.18653\/v1\/d18-1262","DOI":"10.18653\/v1\/D18-1262"},{"key":"29","unstructured":"[29] F. Huang and A. Yates, \u201cOpen-domain semantic role labeling by modeling word spans,\u201d Proc. 48th Annual Meeting of the Association for Computational Linguistics, Uppsala, Sweden, pp.968-978, July 2010."},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] Q.T.N. Do, S. Bethard, and M.-F. Moens, \u201cDomain Adaptation in Semantic Role Labeling Using a Neural Language Model and Linguistic Resources,\u201d The IEEE\/ACM Trans. Audio, Speech, Language Process., vol.23, no.11, pp.1812-1823, Nov. 2015. 10.1109\/taslp.2015.2449072","DOI":"10.1109\/TASLP.2015.2449072"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] S. Hartmann, I. Kuznetsov, T. Martin, and I. Gurevych, \u201cOut-of-domain FrameNet Semantic Role Labeling,\u201d Proc. 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain, pp.471-482, April 2017. 10.18653\/v1\/e17-1045","DOI":"10.18653\/v1\/E17-1045"},{"key":"32","unstructured":"[32] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, \u201cGenerative adversarial nets,\u201d Proc. Advances in Neural Information Processing Systems 27, Montreal Canada, pp.2672-2680, Dec. 2014."},{"key":"33","unstructured":"[33] A. Radford, L. Metz, and S. Chintala, \u201cUnsupervised representation learning with deep convolutional generative adversarial networks,\u201d Proc. International Conference on Learning Representations, San Juan, Puerto Rico, pp.1-16, May 2016."},{"key":"34","doi-asserted-by":"crossref","unstructured":"[34] J.-Y. Zhu, T. Park, P. Isola, and A.A. Efros, \u201cUnpaired Image-to-Image ranslation using Cycle-Consistent Adversarial Networks,\u201d Proc. International Conference on Computer Vision, Venice, Italy, pp.2223-2232, Oct. 2017. 10.1109\/iccv.2017.244","DOI":"10.1109\/ICCV.2017.244"},{"key":"35","unstructured":"[35] Y. Ganin and V. Lempitsky, \u201cUnsupervised domain adaptation by backpropagation,\u201d Proc. 32rd International Conference on Machine Learning, Lille, France, pp.1180-1189, July 2015."},{"key":"36","doi-asserted-by":"crossref","unstructured":"[36] E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, \u201cAdversarial Discriminative Domain Adaptation,\u201d Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp.2962-2971, July 2017. 10.1109\/cvpr.2017.316","DOI":"10.1109\/CVPR.2017.316"},{"key":"37","doi-asserted-by":"crossref","unstructured":"[37] W. Wang, S. Feng, W. Gao, D. Wang, and Y. Zhang, \u201cPersonalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning,\u201d Proc. 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, pp.338-348, Oct. 2018. 10.18653\/v1\/d18-1031","DOI":"10.18653\/v1\/D18-1031"},{"key":"38","doi-asserted-by":"crossref","unstructured":"[38] J. Li, W. Monroe, T. Shi, S. Jean, A. Ritter, and D. Jurafsky, \u201cAdversarial Learning for Neural Dialogue Generation,\u201d Proc. the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, pp.2157-2169, Sept. 2017. 10.18653\/v1\/d17-1230","DOI":"10.18653\/v1\/D17-1230"},{"key":"39","doi-asserted-by":"crossref","unstructured":"[39] X. Liu, J. Gao, X. He, L. Deng, K. Duh, and Y.-Y. Wang, \u201cRepresentation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval,\u201d Proc. 2015 Annual Conference of the North American Chapter of the ACL, Denver, Colorado, pp.912-921, June 2015. 10.3115\/v1\/n15-1092","DOI":"10.3115\/v1\/N15-1092"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E102.D\/12\/E102.D_2019EDP7087\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T14:58:17Z","timestamp":1695481097000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E102.D\/12\/E102.D_2019EDP7087\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,1]]},"references-count":39,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2019]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2019edp7087","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,1]]}}}