My name is Ming-Wei Chang. I am currently a Research Scientist at , working on fun research problems related to machine learning and artificial intelligence!
- QUEST is one of the ACL 2023 outstanding papers.
- FRUIT won the NAACL 2022 best new task paper.
- Co-wrote the deep learning for NLP chapter in the 4-th edition AI:A Modern Approach
Selected Projects (Full List)
Here are some fun projects I have worked on with many awesome researchers and students.
Can you pretrained models understand textual and visual knowledge at the same time?
Dreambooth with in-context learning (no fine-tuning required!)
It is possible to parse an website from pixels only.
Propose the first task-specific prompt for retrieval. Eight examples are enough for buidling a pretty good retriever!
FRUIT is a new task about updating text information in Wikipedia. A really fun project! (NAACL 2022 best new task paper)
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 the amazing 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.