Unmatched technology + unparalleled research
We’ve assembled a diverse team of experts in the AI and NLP fields. Their unparalleled research and award-winning breakthroughs continues to set industry standards, taking us closer to our vision: creating a world without language barriers.
Leading voices in the field
Our research team is changing the way humans communicate
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Alon Lavie,
VP of Language Technologies
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André Martins,
VP of Artificial Intelligence Research
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António Lopes,
Junior Research Scientist
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Daan van Stigt,
Junior Research Scientist
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Fabio Kepler,
Senior AI Research Scientist
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Helena Moniz,
Post-doc researcher at FLUL/INESC-ID and Quality Research
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João Graça,
Co-founder and CTO
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M. Amin Farajian,
Senior Research Engineer
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Miguel Vera,
Junior Research Scientist
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Paulo Dimas,
VP of Product Innovation
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2015, 2017
Most Innovative Company
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2016, 2019
Best Global Machine Translation Quality Estimation System
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2019
Blending Human & Artificial Intelligence, in partnership with Concentrix, UK
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2019
CBInsights AI List of Most Innovative Artificial Intelligence Startups for Disruptive Technology of the Year
Projects & publications
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MT-Telescope
MT-Telescope provides a fine-grained, visual comparison of the quality performance of two machine translation (MT) systems. It lifts the hood on the automatic quality score, allowing users to filter quality performance by keywords, terminology, and segment length. MT-Telescope is available as open-source to benefit the wider MT R&D community.
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COMET
COMET (Crosslingual Optimized Metric for Evaluation of Translation) is a new neural framework for training multilingual machine translation (MT) evaluation models. COMET predicts human judgments of MT quality. This “ready to use” trained COMET model is available as open-source to benefit the wider MT R&D community.
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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). Terminology, on-the-fly domain adaptation, better documentation and GPU optimization are the focus areas in this Marian iteneration.
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MT4ALL
Aims at building data for under-resourced languages in fields of public interest, such as Health and Justice. It’ll 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).
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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).
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OpenKiwi
Quality estimation (QE) is one of the challenges in MT: it evaluates a 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. The accompanying paper won the best system paper at ACL 2019.
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APE-QUEST
Setting up a quality gate and crowdsourcing workflow to improve translation quality in specific domains. 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
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? Created to answer the need for quality translation in health-crisis scenarios, INTERACT is an interdisciplinary European project.
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Unbabel Scribe
Transcription can be a big piece of translation flows, especially when it comes to audiovisual content. This project aims to research & develop a technical solution for automatic transcription and translation of audiovisual content by leveraging a community of human translators (P2020 Co-Funded Project).
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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.