We describe the MAPA project, funded under the Connecting Europe Facility programme, whose goal is the development of an open-source de-identification toolkit for all official European Union languages. It will be developed since January 2020 until December 2021.

The Neural Translation for the European Union (NTEU) project aims to build a neural engine farm with all European official language combinations for eTranslation, without the necessity to use a high-resourced language as a pivot. NTEU started in September 2019 and will run until August 2021.

This paper describes corpora collection activity for building large machine translation systems for Latvian e-Government platform. We describe requirements for corpora, selection and assessment of data sources, collection of the public corpora and creation of new corpora from miscellaneous sources. Methodology, tools and assessment methods are also presented along with the results achieved, challenges faced and conclusions made. Several approaches to address the data scarceness are discussed. We summarize the volume of obtained corpora and provide quality metrics of MT systems trained on this data. Resulting MT systems for English-Latvian, Latvian English and Latvian Russian are integrated in the Latvian e-service portal and are freely available on website HUGO.LV. This paper can serve as a guidance for similar activities initiated in other countries, particularly in the context of European Language Resource Coordination action.

The European Union is a great source of high quality documents with translations into several languages. Parallel corpora from its publications are frequently used in various tasks, machine translation in particular. A source that has not systematically been explored yet is the EU Bookshop ― an online service and archive of publications from various European institutions. The service contains a large body of publications in the 24 official of the EU. This paper describes our efforts in collecting those publications and converting them to a format that is useful for natural language processing in particular statistical machine translation. We report our procedure of crawling the website and various pre-processing steps that were necessary to clean up the data after the conversion from the original PDF files. Furthermore, we demonstrate the use of this dataset in training SMT models for English, French, German, Spanish, and Latvian.

The META-NORD project has contributed to an open infrastructure for language resources (data and tools) under the META-NET umbrella. This paper presents the key objectives of META-NORD and reports on the results achieved in the first year of the project. META-NORD has mapped and described the national language technology landscape in the Nordic and Baltic countries in terms of language use, language technology and resources, main actors in the academy, industry, government and society; identified and collected the first batch of language resources in the Nordic and Baltic countries; documented, processed, linked, and upgraded the identified language resources to agreed standards and guidelines. The three horizontal multilingual actions in META-NORD are overviewed in this paper: linking and validating Nordic and Baltic wordnets, the harmonisation of multilingual Nordic and Baltic treebanks, and consolidating multilingual terminology resources across European countries. This paper also touches upon intellectual property rights for the sharing of language resources.