Portfolio

Incorporating world knowledge to document clustering via heterogeneous information networks
Chenguang Wang, Yangqiu Song, Ahmed El-Kishky, Dan Roth, Ming Zhang, and Jiawei Han.
In Proc. 2015 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2015).
[paper] [slides] [video] [code] [data]

We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network.

Crowd-in-the-loop: A hybrid approach for annotating semantic roles
Chenguang Wang, Alan Akbik, Laura Chiticariu, Yunyao Li, Fei Xia, and Anbang Xu.
In Proc. 2017 Conf. on Empirical Methods on Natural Language Processing (EMNLP 2017).
[paper] [data] [slides]

Our experimental evaluation shows that the proposed approach reduces the workload for experts by over two-thirds, and thus significantly reduces the cost of producing SRL annotation at little loss in quality.

Language models with Transformers
Chenguang Wang, Mu Li, and Alexander Smola.
In arXiv preprint arXiv:1904.09408 (arXiv 2019).
[paper] [code] [slides]

Gets more than 4.4k blog views and more than 320 Likes and Retweets on Twitter. Experimental results on the PTB, WikiText-2, and WikiText-103 show that proposed method achieves perplexities between 20.42 and 34.11 on all problems, i.e. on average an improvement of 12.0 perplexity units compared to state-of-the-art LSTMs.