%0 Conference Proceedings %T Regularized Graph Convolutional Networks for Short Text Classification %A Tayal, Kshitij %A Rao, Nikhil %A Agarwal, Saurabh %A Jia, Xiaowei %A Subbian, Karthik %A Kumar, Vipin %Y Clifton, Ann %Y Napoles, Courtney %S Proceedings of the 28th International Conference on Computational Linguistics: Industry Track %D 2020 %8 December %I International Committee on Computational Linguistics %C Online %F tayal-etal-2020-regularized %X Short text classification is a fundamental problem in natural language processing, social network analysis, and e-commerce. The lack of structure in short text sequences limits the success of popular NLP methods based on deep learning. Simpler methods that rely on bag-of-words representations tend to perform on par with complex deep learning methods. To tackle the limitations of textual features in short text, we propose a Graph-regularized Graph Convolution Network (GR-GCN), which augments graph convolution networks by incorporating label dependencies in the output space. Our model achieves state-of-the-art results on both proprietary and external datasets, outperforming several baseline methods by up to 6% . Furthermore, we show that compared to baseline methods, GR-GCN is more robust to noise in textual features. %R 10.18653/v1/2020.coling-industry.22 %U https://aclanthology.org/2020.coling-industry.22 %U https://doi.org/10.18653/v1/2020.coling-industry.22 %P 236-242