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Featured Resources
Whitepaper
Machine Translation Evaluation
With so many competing technologies, how can you be sure any one solution will set your business up for success?
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Webinar
Intro to Neural Machine Translation
Neural Machine Translation is everywhere. What are the advantages of using it over existing technologies? Glad you asked.
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Case Study
Webinar
Research
Whitepaper
Video
All Resources
Case Study
Zendesk + Lilt
Case Study
e2f + Lilt
Case Study
Machine Translation Tools: Full BLEU Evaluation
Whitepaper
MT Quality Evaluation
Research
Adding Interpretable Attention to Neural Translation Models Improves Word Alignment
Thomas Zenkel, Joern Wuebker, John DeNero
Research
Models and Inference for Prefix-Constrained Machine Translation. In ACL.
Joern Wuebker, Spence Green, John DeNero, Sasa Hasan and Minh-Thang Luong. 2016.
Research
Hierarchical Incremental Adaptation for Statistical Machine Translation. In EMNLP.
Joern Wuebker, Spence Green, and John DeNero. 2015.
Research
Beyond Post-editing: Advances in interactive translation environments. In ATA Chronicle.
Spence Green. 2015.
Research
Human Effort and Machine Learnability in Computer Aided Translation. In EMNLP.
Spence Green, Sida Wang, Jason Chuang, Jeffrey Heer, and Christopher D. Manning. 2014.
Research
Predictive Translation Memory: A Mixed-Initiative System for Human Language Translation. In UIST.
Spence Green, Jason Chuang, Jeffrey Heer, and Christopher D. Manning. 2014.
Webinar
What's New With Lilt
Dec. 2018
Webinar
Language Technology Wiki
A Place to Shape Translation and Interpreting Technology
Webinar
Advanced Terminology Management
Webinar
Using Data to Assess MT Technologies
Webinar
Intro to Lilt
Webinar
How to run MT evaluation
Webinar
Tour of the New Editor
Webinar
Intro to Neural Machine Translation
Webinar
Lilt for Enterprises
Webinar
Lilt for Translators
Video
What is Lilt?
Video
The Future of Work: Part 1
Video
The Future of Work: Part 2
Blog: Be in the Know
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