{"type":"ScholarlyArticle","title":"Putting Back the Stops: Integrating Syntax with Neural Topic Models","authors":[{"name":"Mayank Nagda","slug":"mayank-nagda","given":"Mayank","family":"Nagda","affiliation":"Kaiserslautern-Landau , Germany"},{"name":"Sophie Fellenz","slug":"sophie-fellenz","given":"Sophie","family":"Fellenz","affiliation":"Kaiserslautern-Landau , Germany"}],"abstract":"Syntax and semantics are two key concepts for language understanding. Topic models typically represent the semantics of a text corpus, while removing syntactic information during preprocessing. Without preprocessing, the generated topics become uninterpretable because the syntactic words dominate generated topics. To learn interpretable topics while keeping valuable syntactic information, we propose a novel framework  that can simultaneously learn both syntactic and semantic topics from the corpus without requiring any preprocessing. A context network leverages textual dependencies to distinguish between syntactic and semantic words, while a composite VAE topic model learns two sets of topics. We demonstrate on seven datasets that our proposed method effectively captures both syntactic and semantic representations of a corpus while outperforming state-of-the-art neural topic models and statistical topic models in terms of topic quality.","doi":"10.24963/ijcai.2024/710","url":"https://ijcai.org/proceedings/2024/710","pdf":"https://ijcai.org/proceedings/2024/0710.pdf","markdown":"https://ijcai.org/proceedings/2024/710.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/710","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":710,"pages":{"first":6424,"last":6432},"keywords":["Natural Language Processing: NLP: Information extraction","Machine Learning: ML: Generative models","Natural Language Processing: NLP: Interpretability and analysis of models for NLP","Machine Learning: ML: Applications"],"videos":[],"references":[{"position":1,"raw":"[Bhatt et al., 2020] Gantavya Bhatt, Hritik Bansal, Rishubh Singh, and Sumeet Agarwal. How much complexity does an RNN architecture need to learn syntax-sensitive depen- dencies? In Proceedings of the 58th Annual Meeting of the ACL: Student Research Workshop, pages 244-254, Online, July 2020.","title":"How much complexity does an RNN architecture need to learn syntax-sensitive dependencies?","authors":"Bhatt","year":"2020","venue":"Proceedings of the 58th Annual Meeting of the ACL: Student Research Workshop","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Blei et al., 2003] David M Blei, Andrew Y Ng, and Michael I Jordan. Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993-1022, 2003.","title":"Latent dirichlet allocation","authors":"Andrew Y David M Blei, Michael I Ng, Jordan","year":"2003","venue":"Journal of machine Learning research","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Boyd-Graber and Blei, 2008] Jordan Boyd-Graber and David Blei. Syntactic topic models. Advances in neural information processing systems, 21, 2008.","title":"Syntactic topic models","authors":"Boyd-Graber Blei, Jordan Boyd-Graber, David Blei","year":"2008","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Broder et al., 1997] Andrei Z Broder, Steven C Glassman, Mark S Manasse, and Geoffrey Zweig. Syntactic clustering of the web. Computer networks and ISDN systems, 29(8- 13):1157-1166, 1997.","title":"Syntactic clustering of the web","authors":"Andrei Z Broder, Mark S Steven C Glassman, Geoffrey Manasse, Zweig","year":"1997","venue":"Computer networks and ISDN systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Brown, 1973] Roger Brown. A first language (pp. 54). Cam- bridge, MA: Harvard University Press, 1973.","title":"A first language","authors":"Roger Brown","year":"1973","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Burkhardt and Kramer, 2019] Sophie Burkhardt and Stefan Kramer. Decoupling sparsity and smoothness in the dirich- let variational autoencoder topic model. J. Mach. Learn. Res., 20(131):1-27, 2019.","title":"Decoupling sparsity and smoothness in the dirichlet variational autoencoder topic model","authors":"Kramer Burkhardt, Sophie Burkhardt, Stefan Kramer","year":"2019","venue":"J. Mach. Learn. Res","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cavnar et al., 1994] William B Cavnar, John M Trenkle, et al. N-gram-based text categorization. In Proceedings of SDAIR-94, 3rd annual symposium on document analysis and information retrieval, volume 161175. Las Vegas, NV, 1994.","title":"N-gram-based text categorization","authors":"John M William B Cavnar, Trenkle","year":"1994","venue":"Proceedings of SDAIR-94, 3rd annual symposium on document analysis and information retrieval","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chelba et al., 2017] Ciprian Chelba, Mohammad Norouzi, and Samy Bengio. N-gram language modeling using recurrent neural network estimation. arXiv preprint arXiv:1703.10724, 2017.","title":"N-gram language modeling using recurrent neural network estimation","authors":"Ciprian Chelba, Mohammad Norouzi, Samy Bengio","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Currey and Heafield, 2019] Anna Currey and Kenneth Heafield. Incorporating source syntax into transformer- based neural machine translation. In Proceedings of the Fourth Conference on Machine Translation (Volume 1: Re- search Papers), pages 24-33, 2019.","title":"Incorporating source syntax into transformerbased neural machine translation","authors":"Heafield Currey, Anna Currey, Kenneth Heafield","year":"2019","venue":"Proceedings of the Fourth Conference on Machine Translation","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Dieng et al., 2016] Adji B Dieng, Chong Wang, Jianfeng Gao, and John Paisley. Topicrnn: A recurrent neural net- work with long-range semantic dependency. arXiv preprint arXiv:1611.01702, 2016.","title":"Topicrnn: A recurrent neural network with long-range semantic dependency","authors":"Chong Adji B Dieng, Jianfeng Wang, John Gao, Paisley","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Dieng et al., 2020] Adji B Dieng, Francisco JR Ruiz, and David M Blei. Topic modeling in embedding spaces. Trans- actions of the ACL, 8:439-453, 2020.","title":"Topic modeling in embedding spaces","authors":"Francisco Jr Adji B Dieng, David M Ruiz, Blei","year":"2020","venue":"Transactions of the ACL","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Duan et al., 2019] Sufeng Duan, Hai Zhao, Junru Zhou, and Rui Wang. Syntax-aware transformer encoder for neural machine translation. In 2019 International Conference on Asian Language Processing (IALP), pages 396-401. IEEE, 2019.","title":"Syntax-aware transformer encoder for neural machine translation","authors":"Duan","year":"2019","venue":"2019 International Conference on Asian Language Processing (IALP)","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Figurnov et al., 2018] Mikhail Figurnov, Shakir Mohamed, and Andriy Mnih. Implicit reparameterization gradients. Advances in neural information processing systems, 31, 2018.","title":"Implicit reparameterization gradients","authors":"Mikhail Figurnov, Mohamed Shakir, Andriy Mnih","year":"2018","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Griffiths et al., 2004] Thomas Griffiths, Mark Steyvers, David Blei, and Joshua Tenenbaum. Integrating topics and syntax. Advances in neural information processing systems, 17, 2004.","title":"Integrating topics and syntax","authors":"Thomas Griffiths, Mark Steyvers, David Blei, Joshua Tenenbaum","year":"2004","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Grimmer and Stewart, 2013] Justin Grimmer and Bran- don M Stewart. Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political analysis, 21(3):267-297, 2013.","title":"Text as data: The promise and pitfalls of automatic content analysis methods for political texts","authors":"Stewart ; Grimmer, Justin Grimmer, Brandon M Stewart","year":"2013","venue":"Political analysis","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Gui et al., 2019] Lin Gui, Jia Leng, Gabriele Pergola, Yu Zhou, Ruifeng Xu, and Yulan He. Neural topic model with reinforcement learning. In Proceedings of the 2019 Conference on EMNLP and the 9th IJCNLP, pages 3478- 3483, 2019.","title":"Neural topic model with reinforcement learning","authors":"Lin Gui, Jia Leng, Gabriele Pergola, Yu Zhou, Ruifeng Xu, Yulan He","year":"2019","venue":"Proceedings of the 2019 Conference on EMNLP and the 9th IJCNLP","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Gupta et al., 2019] Pankaj Gupta, Yatin Chaudhary, Florian Buettner, and Hinrich Schütze. Document informed neural autoregressive topic models with distributional prior. Pro- ceedings of the AAAI Conference on Artificial Intelligence, 33(01):6505-6512, Jul. 2019.","title":"Document informed neural autoregressive topic models with distributional prior","authors":"Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schütze","year":"2019","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Higgins et al., 2017] Irina Higgins, Loïc Matthey, Arka Pal, Christopher P. Burgess, Xavier Glorot, Matthew M. Botvinick, Shakir Mohamed, and Alexander Lerchner. beta- vae: Learning basic visual concepts with a constrained variational framework. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, 2017.","title":"betavae: Learning basic visual concepts with a constrained variational framework","authors":"Irina Higgins, Loïc Matthey, Arka Pal, Christopher P Burgess, Xavier Glorot, Matthew M Botvinick, Shakir Mohamed, Alexander Lerchner","year":"2017","venue":"5th International Conference on Learning Representations, ICLR 2017","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Honnibal and Montani, 2017] Matthew Honnibal and Ines Montani. spaCy: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. To appear, 2017.","title":"Matthew Honnibal and Ines Montani. spaCy: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing","authors":"Montani Honnibal","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Hoyle et al., 2021] Alexander Hoyle, Pranav Goel, Andrew Hian-Cheong, Denis Peskov, Jordan Boyd-Graber, and Philip Resnik. Is automated topic model evaluation bro- ken? the incoherence of coherence. Advances in Neural Information Processing Systems, 34:2018-2033, 2021.","title":"Is automated topic model evaluation broken? the incoherence of coherence","authors":"Alexander Hoyle, Pranav Goel, Andrew Hian-Cheong, Denis Peskov, Jordan Boyd-Graber, Philip Resnik","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Huang et al., 2021] Luyang Huang, Shuyang Cao, Nikolaus Parulian, Heng Ji, and Lu Wang. Efficient attentions for long document summarization. In Proceedings of the 2021 Conference of the North American Chapter of the ACL: Human Language Technologies, pages 1419-1436, Online, June 2021. ACL.","title":"Efficient attentions for long document summarization","authors":"Luyang Huang, Shuyang Cao, Nikolaus Parulian, Ji Heng, Lu Wang","year":"2021","venue":"Proceedings of the 2021 Conference of the North American Chapter of the ACL: Human Language Technologies","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Joo et al., 2020] Weonyoung Joo, Wonsung Lee, Sungrae Park, and Il-Chul Moon. Dirichlet variational autoencoder. Pattern Recognition, 107:107514, 2020.","title":"Weonyoung Joo, Wonsung Lee, Sungrae Park, and Il-Chul Moon. Dirichlet variational autoencoder","authors":"Joo","year":"2020","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Kullback and Leibler, 1951] Solomon Kullback and Richard A Leibler. On information and sufficiency. The annals of mathematical statistics, 22(1):79-86, 1951.","title":"On information and sufficiency","authors":"Leibler ; A Kullback, Leibler","year":"1951","venue":"The annals of mathematical statistics","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Lang, 1995] Ken Lang. Newsweeder: Learning to filter net- news. In Machine Learning Proceedings 1995, pages 331- 339.","title":"Newsweeder: Learning to filter netnews","authors":"Lang ;, Ken Lang","year":"1995","venue":"Machine Learning Proceedings","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"Morgan Kaufmann, San Francisco (CA), 1995.","title":null,"authors":"Morgan Kaufmann","year":"1995","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Larochelle and Lauly, 2012] Hugo Larochelle and Stanislas Lauly. A neural autoregressive topic model. In Advances in Neural Information Processing Systems, volume 25.","title":"Hugo Larochelle and Stanislas Lauly. A neural autoregressive topic model","authors":"Lauly Larochelle","year":"2012","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"Curran Associates, Inc., 2012.","title":null,"authors":"Curran Associates, Inc","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Li et al., 2022] Yewen Li, Chaojie Wang, Zhibin Duan, Dongsheng Wang, Bo Chen, Bo An, and Mingyuan Zhou. Alleviating \"posterior collapse\" in deep topic models via policy gradient. In Advances in Neural Information Pro- cessing Systems, 2022.","title":"Alleviating \"posterior collapse\" in deep topic models via policy gradient","authors":"Li","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Liu et al., 2016] Lin Liu, Lin Tang, Wen Dong, Shaowen Yao, and Wei Zhou. An overview of topic modeling and its current applications in bioinformatics. SpringerPlus, 5(1):1-22, 2016.","title":"An overview of topic modeling and its current applications in bioinformatics","authors":"Lin Liu, Lin Tang, Wen Dong, Shaowen Yao, Wei Zhou","year":"2016","venue":"SpringerPlus","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Loper and Bird, 2002] Edward Loper and Steven Bird. Nltk: The natural language toolkit. arXiv preprint cs/0205028, 2002.","title":"Nltk: The natural language toolkit","authors":"Bird ; Loper, Edward Loper, Steven Bird","year":"2002","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Maas et al., 2011] Andrew L. Maas, Raymond E. Daly, Pe- ter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. Learning word vectors for sentiment analysis. In Pro- ceedings of the 49th Annual Meeting of the ACL: Human Language Technologies, pages 142-150, Portland, Oregon, USA, June 2011. ACL.","title":"Learning word vectors for sentiment analysis","authors":"Andrew L Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, Christopher Potts","year":"2011","venue":"Proceedings of the 49th Annual Meeting of the ACL: Human Language Technologies","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[McAuley and Leskovec, 2013] Julian McAuley and Jure Leskovec. Hidden factors and hidden topics: understand- ing rating dimensions with review text. In Proceedings of the 7th ACM conference on Recommender systems, pages 165-172, 2013.","title":"Hidden factors and hidden topics: understanding rating dimensions with review text","authors":"Leskovec Mcauley, Julian Mcauley, Jure Leskovec","year":"2013","venue":"Proceedings of the 7th ACM conference on Recommender systems","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[McCallum, 2002] Andrew Kachites McCallum. Mallet: A machine learning for languagetoolkit. http://mallet. cs. umass. edu, 2002.","title":"Mallet: A machine learning for languagetoolkit","authors":"Andrew Kachites, Mccallum","year":null,"venue":null,"doi":null,"url":"http://mallet.cs.umass.edu,2002","internal_url":null},{"position":34,"raw":"[Meeks and Weingart, 2012] Elijah Meeks and Scott B Wein- gart. The digital humanities contribution to topic modeling. Journal of Digital Humanities, 2(1):1-6, 2012.","title":"Elijah Meeks and Scott B Weingart. The digital humanities contribution to topic modeling","authors":"Weingart Meeks","year":"2012","venue":"Journal of Digital Humanities","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Miao et al., 2016] Yishu Miao, Lei Yu, and Phil Blunsom. Neural variational inference for text processing. In ICML, pages 1727-1736. PMLR, 2016.","title":"Neural variational inference for text processing","authors":"Yishu Miao, Lei Yu, Phil Blunsom","year":"2016","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Nallapati et al., 2017] Ramesh Nallapati, Igor Melnyk, Ab- hishek Kumar, and Bowen Zhou. Sengen: Sentence gener- ating neural variational topic model, 2017.","title":"Sengen: Sentence generating neural variational topic model","authors":"Ramesh Nallapati, Igor Melnyk, Abhishek Kumar, Bowen Zhou","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Neville et al., 1992] Helen J Neville, Debra L Mills, and Donald S Lawson. Fractionating language: Different neu- ral subsystems with different sensitive periods. Cerebral Cortex, 2(3):244-258, 1992.","title":"Fractionating language: Different neural subsystems with different sensitive periods","authors":"Helen J Neville, Debra L Mills, Donald S Lawson","year":"1992","venue":"Cerebral Cortex","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Nguyen and Luu, 2021] Thong Nguyen and Anh Tuan Luu. Contrastive learning for neural topic model. Advances in Neural Information Processing Systems, 34:11974-11986, 2021.","title":"Contrastive learning for neural topic model","authors":"Luu Nguyen, Thong Nguyen, Anh Tuan Luu","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Pang and Lee, 2005] Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the ACL, 2005.","title":"Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales","authors":"Lee ; Pang, Bo Pang, Lillian Lee","year":"2005","venue":"Proceedings of the ACL","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Panwar et al., 2021] Madhur Panwar, Shashank Shailabh, Milan Aggarwal, and Balaji Krishnamurthy. TAN-NTM: Topic attention networks for neural topic modeling. In Proceedings of the 59th Annual Meeting of the ACL and the 11th IJCNLP(Volume 1: Long Papers), pages 3865-3880, Online, August 2021.","title":"TAN-NTM: Topic attention networks for neural topic modeling","authors":"Madhur Panwar, Shashank Shailabh, Milan Aggarwal, Balaji Krishnamurthy","year":"2021","venue":"Proceedings of the 59th Annual Meeting of the ACL and the 11th IJCNLP","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Redington et al., 1998] Martin Redington, Nick Chater, and Steven Finch. Distributional information: A powerful cue for acquiring syntactic categories. Cognitive science, 22(4):425-469, 1998.","title":"Distributional information: A powerful cue for acquiring syntactic categories","authors":"Martin Redington, Nick Chater, Steven Finch","year":"1998","venue":"Cognitive science","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Rezaee and Ferraro, 2020] Mehdi Rezaee and Francis Fer- raro. A discrete variational recurrent topic model without the reparametrization trick. In Proceedings of the 34th International Conference on Neural Information Process- ing Systems, NIPS'20, Red Hook, NY, USA, 2020.","title":"Mehdi Rezaee and Francis Ferraro. A discrete variational recurrent topic model without the reparametrization trick","authors":"Ferraro Rezaee","year":"2020","venue":"Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS'20","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"Curran Associates Inc. [Röder et al., 2015]","title":null,"authors":"Curran Associates, Inc [ Röder","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":44,"raw":"Michael Röder, Andreas Both, and Alexander Hinneburg. Exploring the space of topic co- herence measures. In Proceedings of the eighth ACM inter- national conference on Web search and data mining, pages 399-408, 2015.","title":"Exploring the space of topic coherence measures","authors":"Michael Röder, Andreas Both, Alexander Hinneburg","year":"2015","venue":"Proceedings of the eighth ACM international conference on Web search and data mining","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Srivastava and Sutton, 2017] Akash Srivastava and Charles Sutton. Autoencoding variational inference for topic mod- els. In International Conference on Learning Representa- tions, 2017.","title":"Autoencoding variational inference for topic mod-els","authors":"Akash Srivastava, Charles Sutton","year":"2017","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Thompson and Mimno, 2020] Laure Thompson and David Mimno. Topic modeling with contextualized word repre- sentation clusters. arXiv preprint arXiv:2010.12626, 2020.","title":"Topic modeling with contextualized word representation clusters","authors":"Laure Thompson, David Mimno","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Wang et al., 2005] Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, and Li Cheng. Exploiting syn- tactic, semantic and lexical regularities in language model- ing via directed markov random fields. In Proceedings of the 22nd ICML, pages 948-955, 2005.","title":"Exploiting syntactic, semantic and lexical regularities in language modeling via directed markov random fields","authors":"Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng","year":"2005","venue":"Proceedings of the 22nd ICML","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Wang et al., 2020] Rui Wang, Xuemeng Hu, Deyu Zhou, Yu- lan He, Yuxuan Xiong, Chenchen Ye, and Haiyang Xu. Neural topic modeling with bidirectional adversarial train- ing. In Proceedings of the 58th Annual Meeting of the ACL, pages 340-350, Online, July 2020.","title":"Neural topic modeling with bidirectional adversarial training","authors":"Rui Wang, Xuemeng Hu, Deyu Zhou, Yulan He, Yuxuan Xiong, Chenchen Ye, Haiyang Xu","year":"2020","venue":"Proceedings of the 58th Annual Meeting of the ACL","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Wu and Khudanpur, 1999] Jun Wu and Sanjeev Khudanpur. Combining nonlocal, syntactic and n-gram dependencies in language modeling. In Sixth European Conference on Speech Communication and Technology, 1999.","title":"Combining nonlocal, syntactic and n-gram dependencies in language modeling","authors":"Khudanpur Wu, Jun Wu, Sanjeev Khudanpur","year":"1999","venue":"Sixth European Conference on Speech Communication and Technology","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Yingzhen and Mandt, 2018] Li Yingzhen and Stephan Mandt. Disentangled sequential autoencoder. In Pro- ceedings of the 35th ICML, volume 80 of PMLR, pages 5670-5679, 10-15 Jul 2018.","title":"Disentangled sequential autoencoder","authors":"Li Yingzhen, Stephan Mandt","year":"2018","venue":"Proceedings of the 35th ICML","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Zaheer et al., 2017] Manzil Zaheer, Amr Ahmed, and Alexander J. Smola. Latent LSTM allocation: Joint cluster- ing and non-linear dynamic modeling of sequence data. In Proceedings of the 34th ICML, volume 70 of PMLR, pages 3967-3976, 06-11 Aug 2017.","title":"Latent LSTM allocation: Joint clustering and non-linear dynamic modeling of sequence data","authors":"Manzil Zaheer, Amr Ahmed, Alexander J Smola","year":"2017","venue":"Proceedings of the 34th ICML","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Zhang et al., 2015] Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. Character-level convolutional networks for text classification. In Advances in Neural Information Pro- cessing Systems 28: Annual Conference on Neural Infor- mation Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, pages 649-657, 2015.","title":"Character-level convolutional networks for text classification","authors":"Xiang Zhang, Junbo, Jake Zhao, Yann Lecun","year":"2015","venue":"Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Zhang et al., 2018] Hao Zhang, Bo Chen, Dandan Guo, and Mingyuan Zhou. WHAI: Weibull hybrid autoencoding inference for deep topic modeling. In International Confer- ence on Learning Representations, 2018.","title":"WHAI: Weibull hybrid autoencoding inference for deep topic modeling","authors":"Hao Zhang, Bo Chen, Dandan Guo, Mingyuan Zhou","year":"2018","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Zhao et al., 2021] He Zhao, Dinh Phung, Viet Huynh, Yuan Jin, Lan Du, and Wray L. Buntine. Topic modelling meets deep neural networks: A survey. CoRR, abs/2103.00498, 2021.","title":"Topic modelling meets deep neural networks: A survey","authors":"Zhao","year":"2021","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2021/638"}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}