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Computational Linguistics

Machine translation, multilingual systems, and natural-language processing

Areas of focus for Microsoft Research’s inquiries into computational linguistics are threefold: machine translation, to create systems and technologies that cater to today’s multitude of translation scenarios; multilingual systems, to develop a natural-language-neutral approach to all aspects of linguistic computing; and natural-language processing, to design and build software that will analyze, understand, and generate languages that humans use naturally, with the goal of enabling a user to address a computer as though addressing another person.



Xiaohu Liu and Ruhi Sarikaya, A Discriminative Model Based Entity Dictionary Weighting Approach for Spoken Language Understanding, IEEE – Institute of Electrical and Electronics Engineers, December 2014

Qi Li, Gokhan Tur, Dilek Hakkani-Tur, Xiang Li, Tim Paek, Asela Gunawardana, and Chris Quirk, Distributed open-domain conversational understanding framework with domain-independent extractors, in Proceedings of Spoken Language Technology Workshop, IEEE – Institute of Electrical and Electronics Engineers, December 2014

Xiaodong He, Jianfeng Gao, and Li Deng, Deep Learning for Natural Language Processing: Theory and Practice (Tutorial), CIKM, November 2014

Jiajun Zhang, Shujie Liu, Mu Li, Ming Zhou, and Chengqing Zong, Machine Translation by Minimizing the Semantic Gap in the Vector Embedding Space, in ACM Transactions on Asian Language Information Processing, ACM – Association for Computing Machinery, November 2014

Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Gregoire Mesnil, A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval , CIKM, November 2014

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