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Alon Lavie

VP of Language Technologies

Alon Lavie recently joined Unbabel as the VP of Language Technologies. From June 2015 to March 2019, Alon was a Senior Manager at Amazon, where he led and managed the Amazon Machine Translation R&D team in Pittsburgh, PA. Prior to joining Amazon, Alon was a co-founder of "Safaba Translation Solutions", an MT technology start-up, and served the company as Chairman, President and CTO. Safaba developed automated translation solutions for large global enterprises and was acquired by Amazon in June 2015. For almost 20 years (1996-2015) Alon was a Research Professor at the Language Technologies Institute at Carnegie Mellon University where he continues to be affiliated as an adjunct Consulting Professor. Alon served as the President of the International Association for Machine Translation (IAMT) (2013-2015), was president of the Association for Machine Translation in the Americas (AMTA) (2008-2012), and was General Chair of the AMTA 2010 and 2012 conferences. He is also a member of the Association for Computational Linguistics (ACL), where he was president of SIGParse - ACL's special interest group on parsing (2008-2013). He holds a PhD in Computer Science from Carnegie Mellon University.

Papers published


COMET: A Neural Framework for MT Evaluation

  • Ricardo Rei
  • Craig Stewart
  • Ana C Farinha
  • Alon Lavie

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) 2020


Humor Recognition and Humor Anchor Extraction.

Conference on Empirical Methods in Natural Language Processing (EMNLP-2015) 2015


Cognitive Demand and Cognitive Effort in Post-Editing.

Association for Machine Translation in the Americas (AMTA-2014) 2014


  • Unbabel4EU

    We’re working on advancing European language engines for borderless business communication. Create Europe’s Translation Layer, specifically, by enabling seamless human-quality translation between any pairing of the 24 official languages of the EU in different content types such as Email, Chat and Listings (P2020 Co-Funded Project).

  • User-Focused Marian

    Improve the pre-existing neural machine translation toolkit “Marian” to address the needs of CEF eTranslation and to broaden its user base (H2020 Co-Funded Project). Terminology, on-the-fly domain adaptation, better documentation and GPU optimization are the focus areas in this Marian iteneration.