Haw-Shiuan Chang

Conference

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H.-S. Chang, J. Yuan, M. Iyyer, and A. McCallum,

“Changing the Mind of Transformers for Topically-Controllable Language Generation,”

Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2021.

Oral Presentation

(Paper, Code, Talk, Slides, Poster)

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R. Paul*, H.-S. Chang*, and A. McCallum,

“Multi-facet Universal Schema,”

Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2021.

Oral Presentation

(Paper, Code, Talk, Slides, Poster)

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H.-S. Chang, A. Agrawal, and A. McCallum,

“Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications,”

Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2021.​

(Paper, Slides, Poster)

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H.-S. Chang, Z. Wang, L. Vilnis, and A. McCallum,

“Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection,”

Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT/NAACL), 2018.

(Paper, Code, Demo, Poster)

 

H.-S. Chang, E. Learned-Miller, and A. McCallum,

“Active Bias: Training a More Accurate Neural Network by Emphasizing High Variance Samples,”

Advances in Neural Information Processing Systems (NIPS), 2017.

(Paper, Poster)

 

H.-S. Chang, A. Munir, A. Liu, J. T.-Z. Wei, A. Traylor, A. Nagesh, N. Monath, P. Verga, E. Strubell and A. McCallum, “Extracting Multilingual Relations under Limited Resources: TAC 2016 Cold-Start KB construction and Slot-Filling using Compositional Universal Schema,” Proceedings of TAC, 2016. 

(Paper)

 

H.-S. Chang, H.-J. Hsu and K.-T. Chen,

“Modeling Exercise Relationships in E-Learning: A Unified Approach,”

International Conference on Educational Data Mining (EDM), 2015. 

(Paper, Slides, Demo, Dataset)

H.-S. Chang and Y.-C. F. Wang,

“Simple-to-Complex Discriminative Clustering for Hierarchical Image Segmentation,”

Asian Conference on Computer Vision (ACCV), Singapore, Nov, 2014. 
(Paper, Supplement, Poster, Code)

 

H.-S. Chang and Y.-C. F. Wang,

“Superpixel-Based Large Displacement Optical Flow,”

IEEE International Conference on Image Processing (ICIP), Melbourne, Australia, Sept. 2013.
(Paper, Slides, Poster, Code)

 

 

Journal

 

H.-S. Chang, S. Vembu, S. Mohan, R. Uppaal, and A. McCallum, 

"Using Error Decay Prediction to Overcome Practical Issues of Deep Active Learning for Named Entity Recognition," 

Machine Learning, 109, 1749–1778, 2020

(Paper, Slides, Talk)

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H.-S. Chang, C.-F. Hsu, T. Hoßfeld, and K.-T. Chen,

“Active Learning for Crowdsourcing Multidimensional-QoE Modeling,”

IEEE Transactions on Multimedia (TMM), 20.12, pp. 3337-3352, 2018

(Paper)

 

H.-S. Chang and Y.-C. F. Wang,

“Optimizing the Decomposition of Multiple Foreground Cosegmentation,”

Computer Vision and Image Understanding (CVIU), 141, pp. 18-27, 2015.  

(Paper, Code)

 

W.-T. Li, H.-S. Chang, K.-C. Lien, H.-T. Chang, and Y.-C. F. Wang,

“Exploring Visual and Motion Saliency for Automatic Video Object Extraction,”

IEEE Transactions on Image Processing (TIP), 22(7), pp. 2600-2610, July 2013. 

(Paper)

 

 

Workshop

 

H.-S. Chang, A. Agrawal, A. Ganesh, A. Desai, V. Mathur, A. Hough, and A. McCallum,

“Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings,”

TextGraphs-12: the Workshop on Graph-based Methods for Natural Language Processing, 2018.

(Paper, Slides)

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S. Mysore, E. Kim, E. Strubell, A. Liu, H.-S. Chang, S. Kompella, K. Huang, A. McCallum, and E. Olivetti, 
“Automatically Extracting Action Graphs from Materials Science Synthesis Procedures,”

NIPS Workshop on Machine Learning for Molecules and Materials, 2017. 

(Paper)