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
João Graça
Co-Founder and Chief Technology Officer
André Martins
VP of Research
Paulo Dimas
VP of Product Innovation
Helena Moniz
President of the European Association for Machine Translation and Linguistic consultant
José Souza
Senior AI Research Scientist
M. Amin Farajian,
AI Manager
Fabio Kepler,
Senior AI Research Scientist
Ricardo Rei,
AI Research Engineer
Catarina Farinha,
AI Research Engineer
João Alves,
AI Research Engineer
Daan van Stigt,
AI Research Engineer
Miguel Vera,
AI Research Engineer
Maria Ana Henriques,
R&D Project Manager
Nuno André,
Grants Coordinator
Christine Maroti,
Senior AI Research Engineer
Vera Cabarrão,
Senior Natural Language Analyst
Craig Stewart,
Research Scientist in R&D
Marina Sánchez Torrón,
Senior Natural Language Analyst
Almut Silja Hildebrand,
Senior Research Engineer
Marianna Buchicchio,
Linguistic Services Team Lead
António Lopes,
AI Research Scientist
Maximilian Kohl,
Senior Product Manager
Joseph Szurley
Senior Research Engineer
João Godinho
AI Research Engineer
Eduardo Farah
AI Manager
Pedro Mota
Senior AI Research Engineer
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
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.
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 iteneration.
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).
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.
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).
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.
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.
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
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
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).
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).
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.
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)
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
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.
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,
2019
Best System Demonstration Award
Association for Computational Linguistics,
2019
Blending Human & Artificial Intelligence
Blending Human & Artificial Intelligence, in partnership with Concentrix, UK, National Innovation Awards,
2019
Best Innovation in Customer Service
Best Innovation in Customer Service, in partnership with Concentrix, ECCCSA – European Contact Center and Customer Service Awards,
2019
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,
2019
Most Innovative Artificial Intelligence Startups for Disruptive Technology
List of Most Innovative Artificial Intelligence Startups for Disruptive Technology of the Year, CBInsights,
2019
Most Innovative Companies
Fast Company’s Annual List of the World’s Most Innovative Companies for 2020, Fast Company,
2020
Product of the Year Award Winner
Product of the Year Award Winners, presented by CUSTOMER magazine,
2021
Best Explainability Approach Award
Workshop on Evaluation & Comparison of NLP Systems, Co-located at EMNLP,
2021