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Research at Unbabel

Where machine learning meets human ingenuity

Our research team is advancing the state of the art — and changing the way humans work for the better.

Leading voices in the field

At Unbabel, we’ve built up a team of area experts from around the world, with particular strengths in natural language processing.

João Graça

Founder and CTO

André Martins

VP of Artificial Intelligence Research

Paulo Dimas

VP of Product Innovation

Alon Lavie

VP of Language Technologies

Recent publications

We support our researchers as they place their innovative work in top publications. You can see some recent highlights from our team below.

2019

Adaptively Sparse Transformers

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) 2019

Translator2Vec: Understanding and Representing Human Post-Editors

Proceedings of Machine Translation Summit XVII Volume 1: Research Track 2019

Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)

  • Agata Savary
  • Carla Parra Escartín
  • Francis Bond
  • Jelena Mitrović
  • Verginica Mititelu

None 2019

Unbabel's Participation in the WMT19 Translation Quality Estimation Shared Task

Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2) 2019

Unbabel's Submission to the WMT2019 APE Shared Task: BERT-Based Encoder-Decoder for Automatic Post-Editing

Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2) 2019

Projects

All our research is designed for maximum impact. For our contributions to natural language processing and applied AI, we’ve received numerous awards.

MAIA

MAIA will employ cutting-edge machine learning and natural language processing technologies to build multilingual AI agent assistants, eliminating language barriers. MAIA's 'translation layer' will empower human agents to provide customer support in real-time, in any language, with human quality.

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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).

MT4ALL

Contribute to the CEF Automated Translation Building block by enlarging its coverage for language pairs and domains for which parallel data does not exist (H2020 Co-Funded Project).

Unbabel4EU

Create Europe’s Translation Layer by enabling seamless human-quality translation between any pairing of the 24 official languages of the EU (P2020 Co-Funded Project).

OpenKiwi

Quality estimation (QE) is one of the missing pieces of machine translation: its goal is to evaluate a translation system’s quality without access to reference translations. We released OpenKiwi, a PyTorch-based open-source framework that implements the best QE systems from WMT 2015-18 shared tasks, making it easy to experiment with these models under the same framework. The accompanying paper won the best system paper at ACL 2019.

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APE-QUEST

Boost CEF eTranslation with Automated Post-Editing (APE) & Quality Estimation (QE) for Electronic Exchange of Social Security Information (EESSI) and Online Dispute Resolution (ODR) DSIs and related national services (H2020 Co-Funded Project).

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INTERACT - International Network On Crisis Translation

Timely and accurate communication is essential for crisis management, but what if the only information available to you is in a language you cannot understand?

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Unbabel Scribe

Research & develop a solution for automatic transcription and translation of audiovisual content combined with a community of human translators (P2020 Co-Funded Project).

DeepSPIN

Deep learning is revolutionizing the field of Natural Language Processing (NLP), with breakthroughs in machine translation, speech recognition, and question answering. New language interfaces (digital assistants, messenger apps, customer service bots) are emerging as the next technologies for seamless, multilingual communication among humans and machines.

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Most Innovative Company

2017, 2015

Best Global Machine Translation Quality Estimation System

2019, 2016

Blending Human & Artificial Intelligence, in partnership with Concentrix, UK

2019

CBInsights AI List of Most Innovative Artificial Intelligence Startups for Disruptive Technology of the Year

2019

Want to do research with us?

Our AI team is growing — and we’re looking for candidates with unique skills and stories.