E.g., 09/22/2018
E.g., 09/22/2018
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The recent trend of using deep learning to solve a wide variety of problems in Artificial Intelligence has also reached machine translation - thus establishing a new state-of-the-art approach for this application. This approach is not yet settled by any means. New neural architectures are proposed and ideas coming from such diverse fields as computer vision, game playing, and speech recognition can be applied to machine translation as well.

At the practical end, we are just learning about the deployment challenges of this technology, since old methods, for example, to integrate terminology databases or domain adaptation no longer apply. Just a few years ago, a few million sentences of bilingual data was considered a large amount of data when building state-of-the-art MT engines. Today’s MT engines are now using hundreds of millions and even billions of sentences of data. New techniques have been developed for gathering, creating and synthesizing data.

This presentation will give an overview of the latest developments in research and what this means for practical deployment.

Dion Wiggins

Dion Wiggins, Omniscien Technologies' CTO and Co-Founder, is a highly experienced ICT industry visionary, entrepreneur, analyst and consultant. He has comprehensive knowledge in the fields of software development, architecture and management, as well as an in-depth understanding of Asian ICT markets. He is an accomplished speaker and has a high media profile for his perceptive analysis of ICT in Asia Pacific. Previously Dion was Vice President and Research Director for Gartner based in Hong Kong, where his research reports on ICT in China had a crucial impact on how the world views this market. Dion is also a well-known pioneer of the Asian Internet Industry, being the founder of one of Asia's first ever ISPs (Asia Online in Hong Kong). In his role as consultant, Dion advised literally hundreds of enterprises on their ICT strategy.

Philipp Koehn

Philipp Koehn is Chief Scientist at Omniscien Technologies and Professor of Computer Science at Johns Hopkins University; he also holds the Chair for Machine Translation in the School of Informatics at the University of Edinburgh. Koehn is a leader in the field of statistical MT research with over 100 publications. He is the author of the textbook in the field. Under his leadership the open source Moses system has become the de-facto standard toolkit for MT in research and commercial deployment. Koehn led international research projects such as Euromatrix and CASMACAT; his research has been funded by the European Union, DARPA, Google, Facebook, Amazon, Bloomberg, and several other funding agencies. Koehn received his PhD in 2003 from the University of Southern California and was a postdoctoral research associate at MIT. He was a finalist for the European Patent Office’s European Inventor Award in 2013 and received the Award of Honor from the International Association of Machine Translation in 2015. At Omniscien Koehn refined MT technology for use in real-world deployments and helped develop methods for data acquisition and refinement. Koehn continues to drive innovation and technological development at Omniscien.

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