Microsoft Corporation Beihang University / University of California / /
IndustryTerm
online fashion / web-search language understanding datasets / discriminative algorithms / linear chain / language systems / on-line decoding process / natural language processing tasks / respective systems / neural networks / context-dependent deep neural networks / air travel domain / kernal deep convex networks / deep neural network / convolutional networks / language processing / feed-forward neural networks / deployed spoken language understanding systems / deep belief networks / neural network / spoken language systems / air travel information service / sentence-level neural networks / search queries / travel domain dataset / people making travel reservations / deep neural networks / /
Organization
Microsoft Corporation Beihang University / University of California at San Diego / Institute for Cognitive Science / Association for Computational Linguistics / /
Person
M. Hwang / C. Quirk / M. Galley / Dong Yu / Morgan Kaufmann / M. Auli / Y. Shi / Semi-Markov Conditional Random / W.T. Yih / T. Mikolov / K. Yao / G. Zweig / /
Position
director / feed-forward / above forward / /
Product
Microsoft Corporation Portable Audio Device / CRF Model 1 96.41 / /
ProvinceOrState
California / /
PublishedMedium
Computational Linguistics / Journal of Machine Learning Research / /
RadioStation
DNN / /
Technology
speech recognition / Machine Translation / Machine Learning / natural language processing / neural network / Generative and discriminative algorithms / using statistical machine translation / /
RECURRENT CONDITIONAL RANDOM FIELD FOR LANGUAGE UNDERSTANDING Kaisheng Yao1 , Baolin Peng1,2 , Geoffrey Zweig1 , Dong Yu1 , Xiaolong Li1 , and Feng Gao1 1 2