Kewei Tu, Assistant Professor

Kewei Tu, Assistant Professor

Tel:  (021) 20685089
Email: tukw@@shanghaitech.edu.cn
Office: Room 1A-304B, SIST Building
Major: CS
Website: http://faculty.sist.shanghaitech.edu.cn/faculty/tukw/
Education: Ph.D., Iowa State University, USA
Kewei Tu Research Group Recruitment (Click Here)

RESEARCH INTERESTS

  • Natural Language Processing

  • Machine Learning

  • Knowledge Representation

  • Computer Vision

  • Artificial Intelligence


BIOGRAPHY

Dr. Kewei Tu is an Assistant Professor with the School of Information Science and Technology at ShanghaiTech University, China. He received BS and MS degrees in Computer Science and Technology from Shanghai Jiaotong University, China in 2002 and 2005 respectively and received a PhD degree in Computer Science from Iowa State University, USA in 2012. During 2012-2014, he worked as a postdoctoral researcher at the Center for Vision, Cognition, Learning and Autonomy, Departments of Statistics and Computer Science of the University of California, Los Angeles, USA. His research lies in the areas of natural language processing, machine learning, and artificial intelligence in general, with a focus on the representation, learning and application of stochastic grammars.

See his homepage for more information:
http://faculty.sist.shanghaitech.edu.cn/faculty/tukw/


SELECTED PUBLICATIONS

  1. Wenjuan Han, Ge Wang, Yong Jiang and Kewei Tu, “Multilingual Grammar Induction with Continuous Language Identification”, in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019), Hong Kong, China, November 3–7, 2019.

  2. Yong Jiang, Wenjuan Han and Kewei Tu, “A Regularization-Based Framework for Bilingual Grammar Induction”, in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP 2019), Hong Kong, China, November 3–7, 2019.

  3. Liwen Zhang, Kewei Tu and Yue Zhang, “Latent Variable Sentiment Grammar”, in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy, July 28 – August 2, 2019.

  4. Wenjuan Han, Yong Jiang and Kewei Tu, “Enhancing Unsupervised Generative Dependency Parser with Contextual Information”, in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy, July 28 – August 2, 2019.

  5. Yunzhe Yuan, Yong Jiang and Kewei Tu, “Bidirectional Transition-Based Dependency Parsing”, in Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019), Honolulu, Hawaii, USA, January 27 - February 1, 2019.

  6. Yanpeng Zhao, Liwen Zhang and Kewei Tu, “Gaussian Mixture Latent Vector Grammars”, in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), Melbourne, Australia, July 15–20, 2018. 

  7. Jun Mei, Yong Jiang and Kewei Tu, “Maximum A Posteriori Inference in Sum-Product Networks”, in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), New Orleans, Lousiana, USA, February 2–7, 2018.

  8. Yong Jiang, Yang Zhou and Kewei Tu, “Learningand Evaluation of Latent Dependency Forest Models”, to appear in Neural Computingand Applications.

  9. Yong Jiang, Wenjuan Han and Kewei Tu, “Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, September 7–11, 2017.

  10. Wenjuan Han, Yong Jiang and Kewei Tu, “Dependency Grammar Induction with Neural Lexicalization and Big Training Data”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, September 7–11, 2017.

  11. Jiong Cai, Yong Jiang and Kewei Tu, “CRF Autoencoder for Unsupervised Dependency Parsing”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, September 7–11, 2017.

  12. Xiao Zhang, Yong Jiang, Hao Peng, Kewei Tu and Dan Goldwasser, ‘Semi-supervised Structured Prediction with Neural CRF Autoencoder”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Copenhagen, Denmark, September 7–11, 2017.

  13. Shanbo Chu, Yong Jiang and Kewei Tu, “Latent Dependency Forest Models”, in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI 2017), San Francisco, California, USA, February 4–9, 2017.

  14. Lin Qiu, Kewei Tu and Yong Yu, “Context-Dependent Sense Embedding”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2016), Austin, Texas, USA, November 1-5, 2016.

  15. Yong Jiang, Wenjuan Han and Kewei Tu, “Unsupervised Neural Dependency Parsing”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2016), Austin, Texas, USA, November 1-5, 2016.

  16. Kewei Tu, “Modified Dirichlet Distribution: Allowing Negative Parameters to Induce Stronger Sparsity”, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2016), Austin, Texas, USA, November 1-5, 2016. 

  17. Kewei Tu, “Stochastic And-Or Grammars: A Unified Framework and Logic Perspective”, in Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), New York City, USA, July 9-15, 2016.

  18. Kewei Tu, Meng Meng, Mun Wai Lee, Tae Eun Choe, and Song-Chun Zhu, “Joint Video and Text Parsing for Understanding Events and Answering Queries”, in Proceedings of IEEE MultiMedia, vol. 21, no. 2, pp. 42-70, 2014.