My name is Ming-Wei Chang. I am currently a Research Scientist at Google Research, working on research problems related to machine learning, natural language processing and artificial intelligence in general.
I am really honored to help write the Chapter 24 in the fourth edition of the Artificial Intelligence: A Modern Approach link
Selected Projects (Publication List (Google Scholar))
Here are some fun projects I have worked on with many awesome researchers and students.
BERT is a framework for pre-training deep bidirectional representations from unlabeled text. BERT achieves state-of-the-art results for 11 nlp tasks when it was published. (NAACL 2019 best paper)
Zero-shot entity linking. paper
The power of text understanding makes zero-shot entity linking finally possible. (ACL 2019 best paper candidate)
Semantic parsing for knownledge base. paper
By applying an advanced entity linking system and a deep convolutional neural network model, this semantic parsing system outperformed previous methods substantially when it was published. (ACL 2015 outstanding paper)
Semantic parsing using weak supervision. paper
This project shows that learning with a weak feedback signal is capable of producing strong semantic parsers. This was very surprisingly to me at that time.
Load forecasting using SVM. paper
My first (or second?) research project (in 2001!) under the supervision of Chih-Jen Lin. In this project, we use SVM to predict the power needed to balance the supply and load for powerplants. Winner of the EUNITE competition 2001.