Katrin Marheinecke


2020

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The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
Georg Rehm | Katrin Marheinecke | Stefanie Hegele | Stelios Piperidis | Kalina Bontcheva | Jan Hajič | Khalid Choukri | Andrejs Vasiļjevs | Gerhard Backfried | Christoph Prinz | José Manuel Gómez-Pérez | Luc Meertens | Paul Lukowicz | Josef van Genabith | Andrea Lösch | Philipp Slusallek | Morten Irgens | Patrick Gatellier | Joachim Köhler | Laure Le Bars | Dimitra Anastasiou | Albina Auksoriūtė | Núria Bel | António Branco | Gerhard Budin | Walter Daelemans | Koenraad De Smedt | Radovan Garabík | Maria Gavriilidou | Dagmar Gromann | Svetla Koeva | Simon Krek | Cvetana Krstev | Krister Lindén | Bernardo Magnini | Jan Odijk | Maciej Ogrodniczuk | Eiríkur Rögnvaldsson | Mike Rosner | Bolette Pedersen | Inguna Skadiņa | Marko Tadić | Dan Tufiș | Tamás Váradi | Kadri Vider | Andy Way | François Yvon
Proceedings of the 12th Language Resources and Evaluation Conference

Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cross-lingual and cross-cultural communication are still omnipresent. Language Technologies (LTs) are a powerful means to break down these barriers. While the last decade has seen various initiatives that created a multitude of approaches and technologies tailored to Europe’s specific needs, there is still an immense level of fragmentation. At the same time, AI has become an increasingly important concept in the European Information and Communication Technology area. For a few years now, AI – including many opportunities, synergies but also misconceptions – has been overshadowing every other topic. We present an overview of the European LT landscape, describing funding programmes, activities, actions and challenges in the different countries with regard to LT, including the current state of play in industry and the LT market. We present a brief overview of the main LT-related activities on the EU level in the last ten years and develop strategic guidance with regard to four key dimensions.

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European Language Grid: An Overview
Georg Rehm | Maria Berger | Ela Elsholz | Stefanie Hegele | Florian Kintzel | Katrin Marheinecke | Stelios Piperidis | Miltos Deligiannis | Dimitris Galanis | Katerina Gkirtzou | Penny Labropoulou | Kalina Bontcheva | David Jones | Ian Roberts | Jan Hajič | Jana Hamrlová | Lukáš Kačena | Khalid Choukri | Victoria Arranz | Andrejs Vasiļjevs | Orians Anvari | Andis Lagzdiņš | Jūlija Meļņika | Gerhard Backfried | Erinç Dikici | Miroslav Janosik | Katja Prinz | Christoph Prinz | Severin Stampler | Dorothea Thomas-Aniola | José Manuel Gómez-Pérez | Andres Garcia Silva | Christian Berrío | Ulrich Germann | Steve Renals | Ondrej Klejch
Proceedings of the 12th Language Resources and Evaluation Conference

With 24 official EU and many additional languages, multilingualism in Europe and an inclusive Digital Single Market can only be enabled through Language Technologies (LTs). European LT business is dominated by hundreds of SMEs and a few large players. Many are world-class, with technologies that outperform the global players. However, European LT business is also fragmented – by nation states, languages, verticals and sectors, significantly holding back its impact. The European Language Grid (ELG) project addresses this fragmentation by establishing the ELG as the primary platform for LT in Europe. The ELG is a scalable cloud platform, providing, in an easy-to-integrate way, access to hundreds of commercial and non-commercial LTs for all European languages, including running tools and services as well as data sets and resources. Once fully operational, it will enable the commercial and non-commercial European LT community to deposit and upload their technologies and data sets into the ELG, to deploy them through the grid, and to connect with other resources. The ELG will boost the Multilingual Digital Single Market towards a thriving European LT community, creating new jobs and opportunities. Furthermore, the ELG project organises two open calls for up to 20 pilot projects. It also sets up 32 national competence centres and the European LT Council for outreach and coordination purposes.

2017

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CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Daniel Zeman | Martin Popel | Milan Straka | Jan Hajič | Joakim Nivre | Filip Ginter | Juhani Luotolahti | Sampo Pyysalo | Slav Petrov | Martin Potthast | Francis Tyers | Elena Badmaeva | Memduh Gokirmak | Anna Nedoluzhko | Silvie Cinková | Jan Hajič jr. | Jaroslava Hlaváčová | Václava Kettnerová | Zdeňka Urešová | Jenna Kanerva | Stina Ojala | Anna Missilä | Christopher D. Manning | Sebastian Schuster | Siva Reddy | Dima Taji | Nizar Habash | Herman Leung | Marie-Catherine de Marneffe | Manuela Sanguinetti | Maria Simi | Hiroshi Kanayama | Valeria de Paiva | Kira Droganova | Héctor Martínez Alonso | Çağrı Çöltekin | Umut Sulubacak | Hans Uszkoreit | Vivien Macketanz | Aljoscha Burchardt | Kim Harris | Katrin Marheinecke | Georg Rehm | Tolga Kayadelen | Mohammed Attia | Ali Elkahky | Zhuoran Yu | Emily Pitler | Saran Lertpradit | Michael Mandl | Jesse Kirchner | Hector Fernandez Alcalde | Jana Strnadová | Esha Banerjee | Ruli Manurung | Antonio Stella | Atsuko Shimada | Sookyoung Kwak | Gustavo Mendonça | Tatiana Lando | Rattima Nitisaroj | Josie Li
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, the task was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.
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