Lilt Inc.
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| Private | |
| ISIN | 🆔 |
| Industry | Language localisation, Translation, Artificial Intelligence, Machine Translation |
| Founded 📆 | 2015 |
| Founders 👔 |
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| Headquarters 🏙️ | , |
Area served 🗺️ | |
| Members | |
Number of employees | 85 |
| 🌐 Website | https://lilt.com |
| 📇 Address | |
| 📞 telephone | |
Lilt, Inc., or Lilt, is an AI-powered enterprise language translation company based in San Francisco.[1] Lilt's platform combines a translation management system and neural machine translation with human translators to create a "human-in-the-loop" approach to translation.[2] Lilt's platform is used by companies, schools, and government agencies. [3]
History[edit]
The company was founded in 2015 by Spence Green and John DeNero.[4] While working on his Ph.D. in Computer Science in 2011, Green spent time at Google[5] as a software and research intern working on Google Translate. There, he met DeNero, a Senior Research Scientist at Google Translate. From there, the two starting thinking about ways to apply improvements in neural machine translation to localization.
Lilt raised its first round of seed funding in 2016[6], when Redpoint Ventures, Zetta Venture Partners, and XSeed Capital invested $2.35M in the company. Two years later, the company announced its $9.5M Series A funding[7], led by Sequoia Capital. Most recently, Lilt announced its $25M Series B[8] round in May 2020. Lilt was also featured in the 2020 Forbes AI 50 list as one of America’s most promising AI companies. [9]
As of 2020, Lilt has offices in four locations: San Francisco, CA, USA (headquarters); Indianapolis, IN, USA; Dublin, Ireland; and Berlin, Germany.
Technology[edit]
Lilt's technology uses interactive machine translation in its approach to localization[10], in which a human translator is driving the translations while augmented by a neural machine translation engine. Unlike traditional postediting techniques, Lilt's computer-aided translation tool instead makes predictions for a translation and human translators accept or reject machine translation suggestions.[11] Based on those inputs, the engine learns from those changes and updates in real-time.[12]
Where traditional processes require updates to the machine translation engine, Lilt's adaptive engine is updated every time a new translation is completed. According to a 2019 study done by CSA Research, "71% of translators that use machine translation prefer to work with systems like Lilt rather than raw machine translation output."[13]
Research[edit]
Green, S., Heer, J., and Manning, CD. (2013). The efficacy of human post-editing for language translation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '13). Association for Computing Machinery, New York, NY, USA, 439–448.[14]
Green, S., Chuang, J., Heer, J., and Manning, CD. (2014). Predictive translation memory: a mixed-initiative system for human language translation. In Proceedings of the 27th annual ACM symposium on User interface software and technology (UIST '14). Association for Computing Machinery, New York, NY, USA, 177–187.[15]
Green, S., Wang, S. I., Chuang, J., Heer, J., Schuster, S., & Manning, C. D. (2014, October). Human Effort and Machine Learnability in Computer Aided Translation. ACL Anthology.[16]
Wuebker, J., Green, S., and Denero, J. (2015). Hierarchical Incremental Adaptation for Statistical Machine Translation, EMNLP, Lisbon, 2015. Association for Computational Linguistics.[17]
Wuebker, J., Green, S., Denero, J., Hasan, S., and Luong, M. (2016). Models and Inference for Prefix-Constrained Machine Translation, ACL, Berlin, 2016. Association for Computational Linguistics.[18]
Wuebker, J., Simianer, P., and Denero, J. (2018). Compact Personalized Models for Neural Machine Translation, EMNLP, Brussels, 2018. Association for Computational Linguistics.[19]
Simianer, P., Wuebker, J., and Denero, J. (2019). Measuring Immediate Adaptation Performance for Neural Machine Translation, NAACL, Minneapolis, 2019. Association for Computational Linguistics.[20]
Zenkel, T., Wuebker, J., & DeNero, J. (2019, January 31). Adding Interpretable Attention to Neural Translation Models Improves Word Alignment.[21]
Zenkel, T., Wuebker, J., and Denero, J. (2020). End-to-End Neural Word Alignment Outperforms GIZA++, ACL, 2020. Association for Computational Linguistics.[22]
References[edit]
- ↑ "Translators Without Borders Welcomes Lilt as Diamond Sponsor". PR Newswire. Retrieved 2020-11-12.
- ↑ Falstreau, Nathan (Nov 1, 2018). "Translation startup helps businesses bring their presence to new regions using AI". San Francisco Business Times. Retrieved 17 November 2020.
- ↑ "In-Q-Tel partners with AI-human translation company Lilt". The Sociable. Retrieved 18 November 2020.
- ↑ "Lilt". crunchbase.com. Retrieved 2020-11-12.
- ↑ "How Spence Green Co-Founder And CEO Of Lilt Raised $37.5M To Build A Language Translation For The Modern Enterprise?". Asia Tech Daily. Retrieved 2020-11-12.
- ↑ "Lilt Raises USD 2.35m, Hires SDL Veteran". Slator. Retrieved 2020-11-12.
- ↑ "Lilt Raises USD 9.5M Series A Led by Sequoia as Language Industry Back on VC Radar". Slator. Retrieved 2020-11-12.
- ↑ "Lilt raises $25 million for AI enterprise translation tools". Venture Beat. Retrieved 2020-11-12.
- ↑ "AI 50: America's Most Promising Artificial Intelligence Companies". Forbes. 3 July 2020. Retrieved 18 November 2020.
- ↑ Stolzoff, Simone. "Human translators are the perfect microcosm of the future of work". Quartz. Retrieved 17 November 2020.
- ↑ "The Future Of Work Now: The Computer-Assisted Translator And Lilt". Forbes. Retrieved 2020-11-12.
- ↑ "Lilt is building a machine translation business with humans at the core". TechCrunch. Retrieved 2020-11-12.
- ↑ "The State of the Linguist Supply Chain". CSA Research. Retrieved 2020-11-12.
- ↑ "The efficacy of human post-editing for language translation". Retrieved 2020-11-12.
- ↑ "Predictive translation memory: a mixed-initiative system for human language translation". Retrieved 2020-11-12.
- ↑ "Human Effort and Machine Learnability in Computer Aided Translation". Retrieved 2020-11-12.
- ↑ "Hierarchical Incremental Adaptation for Statistical Machine Translation". Retrieved 2020-11-12.
- ↑ "Models and Inference for Prefix-Constrained Machine Translation". Retrieved 2020-11-12.
- ↑ "Compact Personalized Models for Neural Machine Translation". Retrieved 2020-11-12.
- ↑ "Measuring Immediate Adaptation Performance for Neural Machine Translation". Retrieved 2020-11-12.
- ↑ "Adding Interpretable Attention to Neural Translation Models Improves Word Alignment". Retrieved 2020-11-12.
- ↑ "End-to-End Neural Word Alignment Outperforms GIZA++". Retrieved 2020-11-12.
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