M
Ming-Wei Chang
Researcher at Google
Publications - 107
Citations - 65337
Ming-Wei Chang is an academic researcher from Google. The author has contributed to research in topics: Question answering & Parsing. The author has an hindex of 41, co-authored 98 publications receiving 36404 citations. Previous affiliations of Ming-Wei Chang include Microsoft & National Taiwan University.
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
TL;DR: A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
Proceedings ArticleDOI
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
TL;DR: BERT as mentioned in this paper pre-trains deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.
Journal ArticleDOI
Natural Questions: A Benchmark for Question Answering Research
Tom Kwiatkowski,Jennimaria Palomaki,Olivia Redfield,Michael Collins,Ankur P. Parikh,Chris Alberti,Danielle Epstein,Illia Polosukhin,Jacob Devlin,Kenton Lee,Kristina Toutanova,Llion Jones,Matthew Kelcey,Ming-Wei Chang,Andrew M. Dai,Jakob Uszkoreit,Quoc V. Le,Slav Petrov +17 more
TL;DR: The Natural Questions corpus, a question answering data set, is presented, introducing robust metrics for the purposes of evaluating question answering systems; demonstrating high human upper bounds on these metrics; and establishing baseline results using competitive methods drawn from related literature.
Proceedings ArticleDOI
Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base
TL;DR: This work proposes a novel semantic parsing framework for question answering using a knowledge base that leverages the knowledge base in an early stage to prune the search space and thus simplifies the semantic matching problem.
Journal ArticleDOI
Load forecasting using support vector Machines: a study on EUNITE competition 2001
TL;DR: How SVM, a new learning technique, is successfully applied to load forecasting is discussed in detail and some important conclusions are that temperature might not be useful in such a mid-term load forecasting problem and that the introduction of time-series concept may improve the forecasting.