{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T23:33:32Z","timestamp":1768347212078,"version":"3.49.0"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T00:00:00Z","timestamp":1640217600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T00:00:00Z","timestamp":1640217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972426"],"award-info":[{"award-number":["61972426"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2020A1515010536"],"award-info":[{"award-number":["2020A1515010536"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Hong Kong Research Grants Council","award":["PolyU 11204919"],"award-info":[{"award-number":["PolyU 11204919"]}]},{"name":"Internal Research Grant from the Hong Kong Polytechnic University","award":["1.9B0V"],"award-info":[{"award-number":["1.9B0V"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s11280-021-00984-2","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T14:04:08Z","timestamp":1640268248000},"page":"219-238","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Lifelong topic modeling with knowledge-enhanced adversarial network"],"prefix":"10.1007","volume":"25","author":[{"given":"Xuewen","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1610-9599","authenticated-orcid":false,"given":"Yanghui","family":"Rao","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,23]]},"reference":[{"key":"984_CR1","unstructured":"Aletras, N., Stevenson, M.: Evaluating topic coherence using distributional semantics. In: Proceedings of the 10th International Conference on Computational Semantics, pp. 13\u201322 (2013).\u00a0https:\/\/www.aclweb.org\/anthology\/W13-0102\/.\u00a0Accessed 23 Oct 2020"},{"key":"984_CR2","unstructured":"Bengio, Y.: Discussion of the neural autoregressive distribution estimator. In: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, vol. 15, pp. 38\u201339 (2011). http:\/\/proceedings.mlr.press\/v15\/bengio11a\/bengio11a.pdf. Accessed 22 Oct 2020"},{"key":"984_CR3","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993\u20131022 (2003)","journal-title":"Journal of Machine Learning Research"},{"key":"984_CR4","unstructured":"Cai, H., Chen, T., Zhang, W., Yu, Y., Wang, J.: Efficient architecture search by network transformation. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp. 2787\u20132794 (2018). https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI18\/paper\/view\/16755. Accessed 11 Nov 2020"},{"key":"984_CR5","unstructured":"Chaudhry, A., Ranzato, M., Rohrbach, M., Elhoseiny, M.: Efficient lifelong learning with A-GEM. In: Proceedings of the 7th International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=Hkf2_sC5FX. Accessed 1 Nov 2020"},{"key":"984_CR6","doi-asserted-by":"crossref","unstructured":"Chen, D., Mei, J., Wang, C., Feng, Y., Chen, C.: Online knowledge distillation with diverse peers. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence, pp. 3430\u20133437 (2020). https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/5746. Accessed 1 Nov 2020","DOI":"10.1609\/aaai.v34i04.5746"},{"key":"984_CR7","doi-asserted-by":"crossref","unstructured":"Chen, Q., Zhu, X., Ling, Z., Inkpen, D., Wei, S.: Neural natural language inference models enhanced with external knowledge. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 2406\u20132417. Association for Computational Linguistics (2018). https:\/\/aclanthology.org\/P18-1224\/. Accessed 1 Jan 2021","DOI":"10.18653\/v1\/P18-1224"},{"key":"984_CR8","unstructured":"Chen, T., Goodfellow, I.J., Shlens, J.: Net2net: Accelerating learning via knowledge transfer. In: Y. Bengio, Y. LeCun (eds.) Proceedings of the 4th International Conference on Learning Representations (2016). arxiv:1511.05641. Accessed 1 Jan 2021"},{"key":"984_CR9","unstructured":"Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: A deep convolutional activation feature for generic visual recognition. In: Proceedings of the 31th International Conference on Machine Learning, pp. 647\u2013655 (2014)"},{"key":"984_CR10","doi-asserted-by":"publisher","unstructured":"Du, W., Black, A.W.: Data augmentation for neural online chats response selection. In: Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI, pp. 52\u201358 (2018). https:\/\/doi.org\/10.18653\/v1\/w18-5708","DOI":"10.18653\/v1\/w18-5708"},{"key":"984_CR11","doi-asserted-by":"publisher","unstructured":"Fan, W., Guo, Z., Bouguila, N., Hou, W.: Clustering-based online news topic detection and tracking through hierarchical bayesian nonparametric models. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2126\u20132130. ACM (2021). https:\/\/doi.org\/10.1145\/3404835.3462982","DOI":"10.1145\/3404835.3462982"},{"key":"984_CR12","doi-asserted-by":"publisher","unstructured":"Feng, Y., Feng, J., Rao, Y.: Reward-modulated adversarial topic modeling. In: Proceedings of the 25th International Conference on Database Systems for Advanced Applications, vol. 12112, pp. 689\u2013697 (2020). https:\/\/doi.org\/10.1007\/978-3-030-59410-7_47","DOI":"10.1007\/978-3-030-59410-7_47"},{"key":"984_CR13","doi-asserted-by":"publisher","unstructured":"Fu, Y., Feng, Y.: Natural answer generation with heterogeneous memory. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 185\u2013195. Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/v1\/n18-1017","DOI":"10.18653\/v1\/n18-1017"},{"key":"984_CR14","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems vol 27, pp. 2672\u20132680 (2014). https:\/\/proceedings.neurips.cc\/paper\/2014\/hash\/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html. Accessed on 1 Sep 2020"},{"key":"984_CR15","doi-asserted-by":"publisher","unstructured":"Gupta, P., Chaudhary, Y., Buettner, F., Sch\u00fctze, H.: Document informed neural autoregressive topic models with distributional prior. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence, pp. 6505\u20136512 (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33016505","DOI":"10.1609\/aaai.v33i01.33016505"},{"key":"984_CR16","unstructured":"Gupta, P., Chaudhary, Y., Runkler, T.A., Sch\u00fctze, H.: Neural topic modeling with continual lifelong learning. In: Proceedings of the 37th International Conference on Machine Learning, vol. 119, pp. 3907\u20133917 (2020). http:\/\/proceedings.mlr.press\/v119\/gupta20a.html. Accessed 10 Sep 2020"},{"key":"984_CR17","doi-asserted-by":"crossref","unstructured":"Han, X., Dai, Y., Gao, T., Lin, Y., Liu, Z., Li, P., Sun, M., Zhou, J.: Continual relation learning via episodic memory activation and reconsolidation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6429\u20136440 (2020). https:\/\/www.aclweb.org\/anthology\/2020.acl-main.573\/. Accessed 1 Oct 2020","DOI":"10.18653\/v1\/2020.acl-main.573"},{"key":"984_CR18","doi-asserted-by":"publisher","unstructured":"He, S., Liu, C., Liu, K., Zhao, J.: Generating natural answers by incorporating copying and retrieving mechanisms in sequence-to-sequence learning. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 199\u2013208. Association for Computational Linguistics (2017). https:\/\/doi.org\/10.18653\/v1\/P17-1019","DOI":"10.18653\/v1\/P17-1019"},{"key":"984_CR19","doi-asserted-by":"crossref","unstructured":"Hida, R., Takeishi, N., Yairi, T., Hori, K.: Dynamic and static topic model for analyzing time-series document collections. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 516\u2013520 (2018). https:\/\/www.aclweb.org\/anthology\/P18-2082\/. Accessed 20 Sep 2020","DOI":"10.18653\/v1\/P18-2082"},{"key":"984_CR20","unstructured":"Hinton, G.E., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arxiv:1503.02531 (2015). Accessed 1 Sep 2020"},{"key":"984_CR21","doi-asserted-by":"publisher","unstructured":"Hoyle, A., Goel, P., Resnik, P.: Improving neural topic models using knowledge distillation. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 1752\u20131771 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.137","DOI":"10.18653\/v1\/2020.emnlp-main.137"},{"key":"984_CR22","doi-asserted-by":"crossref","unstructured":"Hu, X., Wang, R., Zhou, D., Xiong, Y.: Neural topic modeling with cycle-consistent adversarial training. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 9018\u20139030 (2020). https:\/\/www.aclweb.org\/anthology\/2020.emnlp-main.725\/. Accessed 25 Nov 2020","DOI":"10.18653\/v1\/2020.emnlp-main.725"},{"issue":"6","key":"984_CR23","doi-asserted-by":"publisher","first-page":"3099","DOI":"10.1007\/s11280-020-00823-w","volume":"23","author":"J Huang","year":"2020","unstructured":"Huang, J., Peng, M., Li, P., Hu, Z., Xu, C.: Improving biterm topic model with word embeddings. World Wide Web 23(6), 3099\u20133124 (2020). https:\/\/doi.org\/10.1007\/s11280-020-00823-w","journal-title":"World Wide Web"},{"key":"984_CR24","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, vol. 37, pp. 448\u2013456 (2015). http:\/\/proceedings.mlr.press\/v37\/ioffe15.html. Accessed 1 Oct 2020"},{"key":"984_CR25","doi-asserted-by":"publisher","unstructured":"Jiang, H., Zhou, R., Zhang, L., Wang, H., Zhang, Y.: A topic model based on poisson decomposition. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1489\u20131498. ACM (2017). https:\/\/doi.org\/10.1145\/3132847.3132942","DOI":"10.1145\/3132847.3132942"},{"issue":"6","key":"984_CR26","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.1007\/s11280-018-0639-1","volume":"22","author":"H Jiang","year":"2019","unstructured":"Jiang, H., Zhou, R., Zhang, L., Wang, H., Zhang, Y.: Sentence level topic models for associated topics extraction. World Wide Web 22(6), 2545\u20132560 (2019). https:\/\/doi.org\/10.1007\/s11280-018-0639-1","journal-title":"World Wide Web"},{"key":"984_CR27","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.: Lightgbm: A highly efficient gradient boosting decision tree. In: Proceedings of the 31st Conference on Neural Information Processing Systems, pp. 3146\u20133154 (2017). https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/6449f44a102fde848669bdd9eb6b76fa-Abstract.html. Accessed on 20 Nov 2020"},{"key":"984_CR28","unstructured":"Keskar, N.S., Mudigere, D., Nocedal, J., Smelyanskiy, M., Tang, P.T.P.: On large-batch training for deep learning: Generalization gap and sharp minima. In: Proceedings of the 5th International Conference on Learning Representations (2017). https:\/\/openreview.net\/forum?id=H1oyRlYgg. Accessed 1 Oct 2020"},{"key":"984_CR29","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (2015). arxiv: 1412.6980. Accessed 15 Sep 2020"},{"key":"984_CR30","doi-asserted-by":"crossref","unstructured":"Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., Hadsell, R.: Overcoming catastrophic forgetting in neural networks. In: Proceedings of the National Academy of Sciences, pp. 3521\u20133526 (2017)","DOI":"10.1073\/pnas.1611835114"},{"key":"984_CR31","unstructured":"Lauly, S., Zheng, Y., Allauzen, A., Larochelle, H.: Document neural autoregressive distribution estimation. Journal of Machine Learning Research 18, 113:1\u2013113:24 (2017). http:\/\/jmlr.org\/papers\/v18\/16-017.html. Accessed 20 Sep 2020"},{"key":"984_CR32","doi-asserted-by":"crossref","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. In: Proceedings of the 14th European Conference on Computer Vision, pp. 614\u2013629. Springer (2016)","DOI":"10.1007\/978-3-319-46493-0_37"},{"key":"984_CR33","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.neucom.2020.07.048","volume":"415","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Zhang, W., Wang, J.: Adaptive multi-teacher multi-level knowledge distillation. Neurocomputing 415, 106\u2013113 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2020.07.048","journal-title":"Neurocomputing"},{"key":"984_CR34","doi-asserted-by":"crossref","unstructured":"Madotto, A., Wu, C., Fung, P.: Mem2seq: Effectively incorporating knowledge bases into end-to-end task-oriented dialog systems. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 1468\u20131478. Association for Computational Linguistics (2018). https:\/\/aclanthology.org\/P18-1136\/","DOI":"10.18653\/v1\/P18-1136"},{"key":"984_CR35","doi-asserted-by":"crossref","unstructured":"Marsland, S., Shapiro, J., Nehmzow, U.: A self-organising network that grows when required. Neural Networks 15, 1041\u20131058 (2002). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0893608002000783. Accessed 11 Nov 2020","DOI":"10.1016\/S0893-6080(02)00078-3"},{"key":"984_CR36","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/S0079-7421(08)60536-8","volume":"24","author":"M Mccloskey","year":"1989","unstructured":"Mccloskey, M.: Catastrophic interference in connectionist networks: The sequential learning problem. Psychology of Learning and Motivation 24, 109\u2013165 (1989)","journal-title":"Psychology of Learning and Motivation"},{"key":"984_CR37","unstructured":"Miao, Y., Grefenstette, E., Blunsom, P.: Discovering discrete latent topics with neural variational inference. In: Proceedings of the 34th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol. 70, pp. 2410\u20132419 (2017). http:\/\/proceedings.mlr.press\/v70\/miao17a.html. Accessed 23 Sep 2020"},{"key":"984_CR38","unstructured":"Miao, Y., Yu, L., Blunsom, P.: Neural variational inference for text processing. In: Proceedings of the 33nd International Conference on Machine Learning, vol. 48, pp. 1727\u20131736 (2016). http:\/\/proceedings.mlr.press\/v48\/miao16.html. Accessed 20 Sep 2020"},{"key":"984_CR39","unstructured":"Mimno, D.M., Wallach, H.M., Talley, E.M., Leenders, M., McCallum, A.: Optimizing semantic coherence in topic models. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 262\u2013272 (2011). https:\/\/www.aclweb.org\/anthology\/D11-1024\/. Accessed 1 Oct 2020"},{"key":"984_CR40","doi-asserted-by":"crossref","unstructured":"li\u00a0Ming, G., Song, H.: Adult neurogenesis in the mammalian brain: Significant answers and significant questions. Neuron 70, 687\u2013702 (2011). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0896627311003485. Accessed 15 Nov 2020","DOI":"10.1016\/j.neuron.2011.05.001"},{"key":"984_CR41","unstructured":"Mnih, A., Gregor, K.: Neural variational inference and learning in belief networks. In: Proceedings of the 31th International Conference on Machine Learning, JMLR Workshop and Conference Proceedings, pp. 1791\u20131799. JMLR.org (2014). http:\/\/proceedings.mlr.press\/v32\/mnih14.html. Accessed 10 Sep 2020"},{"key":"984_CR42","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning, pp. 807\u2013814 (2010). https:\/\/icml.cc\/Conferences\/2010\/papers\/432.pdf. Accessed 11 Oct 2020"},{"key":"984_CR43","doi-asserted-by":"publisher","unstructured":"Nan, F., Ding, R., Nallapati, R., Xiang, B.: Topic modeling with wasserstein autoencoders. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, pp. 6345\u20136381 (2019). https:\/\/doi.org\/10.18653\/v1\/p19-1640","DOI":"10.18653\/v1\/p19-1640"},{"key":"984_CR44","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","volume":"113","author":"GI Parisi","year":"2019","unstructured":"Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S.: Continual lifelong learning with neural networks: A review. Neural Networks 113, 54\u201371 (2019). https:\/\/doi.org\/10.1016\/j.neunet.2019.01.012","journal-title":"Neural Networks"},{"key":"984_CR45","doi-asserted-by":"publisher","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1532\u20131543 (2014). https:\/\/doi.org\/10.3115\/v1\/d14-1162","DOI":"10.3115\/v1\/d14-1162"},{"key":"984_CR46","doi-asserted-by":"publisher","unstructured":"Peters, M.E., Neumann, M., IV, R.L.L., Schwartz, R., Joshi, V., Singh, S., Smith, N.A.: Knowledge enhanced contextual word representations. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 43\u201354. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1005","DOI":"10.18653\/v1\/D19-1005"},{"key":"984_CR47","doi-asserted-by":"publisher","unstructured":"Rebuffi, S., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: Proceedings of the 30th Conference on Computer Vision and Pattern Recognition, pp. 5533\u20135542 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.587","DOI":"10.1109\/CVPR.2017.587"},{"issue":"2","key":"984_CR48","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1080\/09540099550039318","volume":"7","author":"AV Robins","year":"1995","unstructured":"Robins, A.V.: Catastrophic forgetting, rehearsal and pseudorehearsal. Connect. Sci. 7(2), 123\u2013146 (1995). https:\/\/doi.org\/10.1080\/09540099550039318","journal-title":"Connect. Sci."},{"key":"984_CR49","doi-asserted-by":"publisher","unstructured":"R\u00f6der, M., Both, A., Hinneburg, A.: Exploring the space of topic coherence measures. In: Proceedings of the 8th ACM International Conference on Web Search and Data Mining, pp. 399\u2013408 (2015). https:\/\/doi.org\/10.1145\/2684822.2685324","DOI":"10.1145\/2684822.2685324"},{"key":"984_CR50","doi-asserted-by":"publisher","unstructured":"Shen, Y., Zeng, X., Jin, H.: A progressive model to enable continual learning for semantic slot filling. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 1279\u20131284 (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1126","DOI":"10.18653\/v1\/D19-1126"},{"key":"984_CR51","unstructured":"Srivastava, A., Sutton, C.: Autoencoding variational inference for topic models. In: Proceedings of the 5th International Conference on Learning Representation (2017). https:\/\/openreview.net\/forum?id=BybtVK9lg. Accessed 19 Sep 2020"},{"key":"984_CR52","doi-asserted-by":"publisher","unstructured":"Venkatesaramani, R., Downey, D., Malin, B.A., Vorobeychik, Y.: A semantic cover approach for topic modeling. In: Proceedings of the 8th Joint Conference on Lexical and Computational Semantics, pp. 92\u2013102 (2019). https:\/\/doi.org\/10.18653\/v1\/s19-1011","DOI":"10.18653\/v1\/s19-1011"},{"key":"984_CR53","doi-asserted-by":"publisher","unstructured":"Wang, H., Xiong, W., Yu, M., Guo, X., Chang, S., Wang, W.Y.: Sentence embedding alignment for lifelong relation extraction. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 796\u2013806 (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1086","DOI":"10.18653\/v1\/n19-1086"},{"key":"984_CR54","doi-asserted-by":"crossref","unstructured":"Wang, R., Hu, X., Zhou, D., He, Y., Xiong, Y., Ye, C., Xu, H.: Neural topic modeling with bidirectional adversarial training. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 340\u2013350 (2020). https:\/\/www.aclweb.org\/anthology\/2020.acl-main.32\/. Accessed 19 Sep 2020","DOI":"10.18653\/v1\/2020.acl-main.32"},{"key":"984_CR55","doi-asserted-by":"publisher","unstructured":"Wang, R., Zhou, D., He, Y.: ATM: adversarial-neural topic model. Information Processing and Management 56 (2019). https:\/\/doi.org\/10.1016\/j.ipm.2019.102098","DOI":"10.1016\/j.ipm.2019.102098"},{"key":"984_CR56","doi-asserted-by":"publisher","unstructured":"Wang, R., Zhou, D., He, Y.: Open event extraction from online text using a generative adversarial network. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 282\u2013291 (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1027","DOI":"10.18653\/v1\/D19-1027"},{"key":"984_CR57","doi-asserted-by":"publisher","unstructured":"Wang, S., Chen, Z., Liu, B.: Mining aspect-specific opinion using a holistic lifelong topic model. In: Proceedings of the 25th International Conference on World Wide Web, pp. 167\u2013176 (2016). https:\/\/doi.org\/10.1145\/2872427.2883086","DOI":"10.1145\/2872427.2883086"},{"key":"984_CR58","doi-asserted-by":"publisher","unstructured":"Yang, P., Li, L., Luo, F., Liu, T., Sun, X.: Enhancing topic-to-essay generation with external commonsense knowledge. In: Proceedings of the 57th Conference of the Association for Computational Linguistics, pp. 2002\u20132012. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/p19-1193","DOI":"10.18653\/v1\/p19-1193"},{"key":"984_CR59","unstructured":"Yu, W., Zhu, C., Li, Z., Hu, Z., Wang, Q., Ji, H., Jiang, M.: A survey of knowledge-enhanced text generation. arxiv:2010.04389 (2020)"},{"key":"984_CR60","unstructured":"Zenke, F., Poole, B., Ganguli, S.: Continual learning through synaptic intelligence. In: Proceedings of the 34th International Conference on Machine Learning, pp. 3987\u20133995 (2017)"},{"key":"984_CR61","doi-asserted-by":"publisher","unstructured":"Zhang, H., Liu, Z., Xiong, C., Liu, Z.: Grounded conversation generation as guided traverses in commonsense knowledge graphs. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 2031\u20132043 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.184","DOI":"10.18653\/v1\/2020.acl-main.184"},{"key":"984_CR62","doi-asserted-by":"publisher","unstructured":"Zhou, H., Young, T., Huang, M., Zhao, H., Xu, J., Zhu, X.: Commonsense knowledge aware conversation generation with graph attention. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 4623\u20134629. ijcai.org (2018) https:\/\/doi.org\/10.24963\/ijcai.2018\/643","DOI":"10.24963\/ijcai.2018\/643"}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-021-00984-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-021-00984-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-021-00984-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T04:53:51Z","timestamp":1645160031000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-021-00984-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,23]]},"references-count":62,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["984"],"URL":"https:\/\/doi.org\/10.1007\/s11280-021-00984-2","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,23]]},"assertion":[{"value":"20 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}