Chris Wendt


Live presentations to a multilingual audience: personal universal translator
Chris Wendt
Proceedings of Machine Translation Summit XVI: Commercial MT Users and Translators Track


Speech translation user experience in practice
Chris Wendt | Will Lewis | Tanvi Surti
Conferences of the Association for Machine Translation in the Americas: MT Users' Track


USE CASE: Customization and Collaboration to Enhance MT for a Knowledge Base Online Portal
Chris Wendt | Federico Garcea
Proceedings of Machine Translation Summit XIV: User track


Machine translation at Microsoft
Chris Wendt
Proceedings of Translating and the Computer 34


Better translations with user collaboration – Integrated MT at Microsoft
Chris Wendt
Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Commercial MT User Program

This paper outlines the methodologies Microsoft has deployed for seamless integration of human translation into the translation workflow, and describes a variety of methods to gather and collect human translation data. Increased amounts of parallel training data help to enhance the translation quality of the statistical MT system in use at Microsoft. The presentation covers the theory, the technical methodology as well as the experiences Microsoft has with the implementation, and practical use of such a system. Included is a discussion of the factors influencing the translation quality of a statistical MT system, a short description of the feedback collection mechanism in use at Microsoft, and the metrics it observed on its MT deployments.

Achieving Domain Specificity in SMT without Overt Siloing
William D. Lewis | Chris Wendt | David Bullock
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We examine pooling data as a method for improving Statistical Machine Translation (SMT) quality for narrowly defined domains, such as data for a particular company or public entity. By pooling all available data, building large SMT engines, and using domain-specific target language models, we see boosts in quality, and can achieve the generalizability and resiliency of a larger SMT but with the precision of a domain-specific engine.


Pushing the Quality of a Customized SMT System Using Shared Training Data
Chris Wendt | Will Lewis
Proceedings of Machine Translation Summit XII: Commercial MT User Program