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") er et banebrydende forskningsprojekt, der er finansieret af ELISE Open Call, og som har til formål at opbygge ansvarlig MT for samtaledata: MT af høj kvalitet til at åbne nye markeder, hvor kritiske MT-fejl ikke kan tolereres.

MAIA vil anvende avanceret maskinlæring og teknologier til behandling af naturligt sprog til at opbygge flersprogede AI-assistenter, der fjerner sprogbarrierer. MAIAs 'oversættelseslag' vil gøre det muligt for menneskelige agenter at yde kundesupport i realtid på alle sprog og med menneskelig kvalitet.

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.

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).

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).

Rettidig og præcis kommunikation er afgørende for krisestyring, men hvad nu, hvis den eneste information, du har adgang til, er på et sprog, du ikke forstår? INTERACT er et tværfagligt europæisk projekt, der er skabt for at imødekomme behovet for kvalitetsoversættelse i scenarier med sundhedskrise.

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).

Deep learning er ved at revolutionere Natural Language Processing (NLP)-området med gennembrud inden for maskinoversættelse, talegenkendelse og spørgeskemabesvarelse. Nye sproggrænseflader (digitale assistenter, messenger-apps, kundeservicebots) er ved at blive de næste teknologier til problemfri, flersproget kommunikation mellem mennesker og maskiner.

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” er et projekt, der drives af Unbabel og er medstiftet af Portugal 2020 – Sistema de Incentivos à Investigação e Desenvolvimento Tecnológico (SI I&DT)

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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.

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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.

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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.

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MT-Telescope giver en finkornet, visuel sammenligning af to maskinoversættelse (MT)-systemers kvalitetspræstationer. Den løfter kølerhjelmen på den automatiske kvalitetsscore og giver brugerne mulighed for at filtrere kvalitetspræstationer efter nøgleord, terminologi og segmentlængde. MT-Telescope er tilgængelig som åben kildekode til gavn for det bredere MT R&D-fællesskab.

COMET (Crosslingual Optimized Metric for Evaluation of Translation) er en ny neural ramme til træning af flersprogede evalueringsmodeller til Maskinoversættelse (MT). COMET forudsiger menneskelige vurderinger af MT-kvalitet. Denne "klar til brug" trænede COMET-model er tilgængelig som åben kildekode til gavn for det bredere MT R&D-fællesskab.

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.