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

  • João Graça, Co-Founder and Chief Technology Officer at Unbabel

    João Graça

    Co-Founder and Chief Technology Officer

  • André Martins

    VP of Research

  • Paulo Dimas

    VP of Product Innovation

  • José Souza

    Staff AI Research Scientist

  • M. Amin Farajian

    Senior AI Research Scientist

  • Fabio Kepler

    Senior AI Research Scientist

  • Ricardo Rei

    Ricardo Rei

    Senior AI Research Scientist

  • Catarina Farinha

    AI Research Manager

  • João Alves

    AI Research Scientist

  • Daan van Stigt

    Daan van Stigt

    Senior AI Research Scientist

  • Maria Ana Henriques

    Maria Ana Henriques

    R&D Project Manager

  • Nuno André

    Nuno André

    Senior Grants Coordinator

  • Vera Cabarrão

    Vera Cabarrão

    Senior AI Quality Manager

  • Marina Sánchez Torrón

    Senior Natural Language Analyst

  • Marianna Buchicchio

    Senior Manager AI Quality

  • Maximilian Kohl

    Maximilian Kohl

    Senior Product Manager

  • João Godinho

    Senior AI Research Engineer

  • Pedro Mota

    Senior AI Research Engineer

  • Nuno Guerreiro

    Nuno Guerreiro

    AI Research Scientist

  • José Pombal

    AI Research Scientist

  • Pedro Martins

    Senior AI Research Scientist

  • Muhammad Bilal

    Muhammad Bilal

    Senior Backend Engineering Manager


  • Center for Responsible AI

    The Center for Responsible AI is one of the largest centers dedicated to Responsible AI, bringing together ten startups, eight research centers, a law firm, and five industry leaders, that will collaborate to develop 21 innovative AI products leveraged by Responsible AI technologies such as equity, explainability, and sustainability. The center is co-funded by the Portuguese PRR.


    UTTER – Unified Transcription and Translation for Extended Reality – is a collaborative Research and Innovation project funded under Horizon Europe that aims to leverage large language models to build the next generation of multimodal eXtended reality (XR) technologies for transcription, translation, summarisation, and minuting. UTTER’s use-case prototypes will cover (i) a personal assistant for meetings that can improve communication in the online world and (ii) an advanced customer service assistant to support global markets.


    QUARTZ (“Quality-Aware Machine Translation”) is a cutting edge research project funded by the ELISE Open Call to build Responsible MT for conversational data: high-quality MT to unlock new markets where critical MT errors can’t be tolerated.

  • 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.

  • 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 iteration.

  • 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).

  • 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).


    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).


    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.

  • 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).

  • 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.

  • Unbabel’s Internationalization Plan

    Unbabel’s Internationalization Plan (“Unbabel 2017-2019: Plano de Internacionalização”) is a project led by Unbabel and co-funded by Portugal 2020 – Sistema de Incentivos à Internacionalização das PME.

  • Unbabel 2017: A new ecosystem of Machine + Crowd Translation

    “Unbabel 2017: A new ecosystem of Machine + Crowd Translation” is a project led by Unbabel and co-funded by Portugal 2020 – Sistema de Incentivos à Investigação e Desenvolvimento Tecnológico (SI I&DT)

    Learn more

Open-source tools


    XCOMET is a cutting-edge, open-source metric designed to be more interpretable and better aligned with MQM human evaluations. XCOMET combines sentence-level evaluation, similar to neural metrics such as COMET and BLEURT, and error span detection capabilities.

    Learn more

  • TowerLLM

    TowerLLM is a suite of multilingual large language models (LLM) optimized for translation-related tasks ranging from pre-translation, to translation and evaluation tasks, such as machine translation (MT), automatic post-editing (APE), and translation ranking. Tower is built on top of LLaMA2 [1], comes in two sizes — 7B and 13B parameters —, and currently supports 10 languages.

    Learn more

  • CometKiwi XL

    CometKiwiXL is large language model (LLM) specialized in predicting the quality of a translation. CometKiwi XL (3.5B) and CometKiwi XXL (10.7B) are the open-sourced versions of our state-of-the-art Quality Estimation model.

    Learn more

  • 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.


    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.

  • 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.


  • Eighth Conference on Machine Translation

    Winners of the WMT 2023 QE Shared Task

  • Eighth Conference on Machine Translation

    Winners of the WMT 2023 Metrics Shared Task

  • COMET22

    Winning submission for the Chinese-English language pair and the second best for the other two language pairs in the WMT2022 Metrics shared task

  • CometKiwi

    Winning submission of the WMT 2022 Quality Estimation (QE) shared task

  • Best Presentation award for the Users and Providers track

    AMTA Conference 2022

  • Best Paper Award

    EAMT Conference 2022 ( Title: Searching for Cometinho: The Little Metric That Could)

  • Most Innovative Company

    Most Innovative Company (at the Game Changer Innovation Contest), TAUS (Translation Automation User Society)
    2015, 2017

  • Best Global Machine Translation Quality Estimation System

    WMT – Conference on Machine Translation,
    2016, 2019

  • Best Global Machine Translation Automatic Post-Editing System

    WMT – Conference on Machine Translation,

  • Best System Demonstration Award

    Association for Computational Linguistics,

  • Blending Human & Artificial Intelligence

    Blending Human & Artificial Intelligence, in partnership with Concentrix, UK, National Innovation Awards,

  • Best Innovation in Customer Service

    Best Innovation in Customer Service, in partnership with Concentrix, ECCCSA – European Contact Center and Customer Service Awards,

  • Best use of AI and associated technologies

    Best use of AI and associated technologies, in partnership with Microsoft, ECCCSA – European Contact Center and Customer Service Awards,

  • Most Innovative Artificial Intelligence Startups for Disruptive Technology

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

  • Most Innovative Companies

    Fast Company’s Annual List of the World’s Most Innovative Companies for 2020, Fast Company,

  • Product of the Year Award Winner

    Product of the Year Award Winners, presented by CUSTOMER magazine,

  • Best Explainability Approach Award

    Workshop on Evaluation & Comparison of NLP Systems, Co-located at EMNLP,


See all publications
Duarte M. Alves, José Pombal, Nuno M. Guerreiro, Pedro H. Martins, João Alves, Amin Farajian, Ben Peters, Ricardo Rei, Patrick Fernandes, Sweta Agrawal, Pierre Colombo, José G.C. de Souza, André F.T. Martins | COLM
Duarte M. Alves, Nuno M. Guerreiro, João Alves, José Pombal, Ricardo Rei, José G. C. de Souza, Pierre Colombo, André F. T. Martins | Findings of EMNLP 2023
Ricardo Rei, Nuno M. Guerreiro, José Pombal, Daan van Stigt, Marcos Treviso, Luisa Coheur, José G. C. de Souza, André Martins | WMT 2023
Nuno M. Guerreiro, Ricardo Rei, Daan van Stigt, Luisa Coheur, Pierre Colombo, André F.T. Martins | Association for Computational Linguistics
Manuel Faysse, Patrick Fernandes, Nuno M. Guerreiro, António Loison, Duarte M. Alves, Caio Corro, Nicolas Boizard, João Alves. Ricardo Rei, Pedro H. Martins, Antoni Bigata Casademunt10 François Yvon, André F.T. Martins, Gautier Viaud, Céline Hudelot, Pierre Colombo | TMLR
Nuno M. Guerreiro, Duarte Alves, Jonas Waldendorf, Barry Haddow, Alexandra Birch, Pierre Colombo, André F. T. Martins | TACL Transactions of the Association for Computational Linguistics
Taisiya Glushkova, Chryssa Zerva, Ricardo Rei, André Martins | Findings of the Association for Computational Linguistics: EMNLP 2021
Patrick Fernandes, Kayo Yin, Graham Neubig, André Martins | 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Ben Peters, André Martins | 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
António Lopes, Amin Farajian, Rachel Bawden, Michael Zhang, André Martins | 22nd Annual Conference of the European Association for Machine Translation
António Góis, André Martins | Machine Translation Summit XVII: Research Track