Put AI to Work
April 15-18, 2019
New York, NY
Chang Ming-Wei

Chang Ming-Wei
Research Scientist, Google

Website

Ming-Wei Chang is a research scientist at Google AI Language. He enjoys developing interesting machine learning algorithms for practical problems, especially in the field of natural language processing. He has published more than 35 papers at top-tier conferences and won an outstanding paper award at ACL 2015 for his work on question answering over knowledge bases. He also won several international machine learning competitions on topics like entity linking, power load forecast prediction, and sequential data classification. His recent paper, “BERT: Pretraining of Deep Bidirectional Transformers for Language Understanding“—cowritten with his colleagues in Google AI Language—demonstrates the power of language model pretraining and details the new state of the art for over 11 natural language processing tasks.

Sessions

1:00pm–1:40pm Thursday, April 18, 2019
BERT: Pretraining deep bidirectional transformers for language understanding
Machine Learning, Models and Methods
 Location: Regent Parlor
Secondary topics:  Models and Methods, Text, Language, and Speech
Chang Ming-Wei (Google)
Average rating: *****
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Ming-Wei Chang offers an overview of a new language representation model called BERT (Bidirectional Encoder Representations from Transformers). Unlike recent language representation models, BERT is designed to pretrain deep bidirectional representations by jointly conditioning on both left and right context in all layers. Read more.