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We present an extension of the SynSemClass event-type ontology, originally conceived as a bilingual Czech-English resource. We added German entries to the classes representing the concepts of the ontology. Having a different starting point than the original work (unannotated parallel corpus without links to a valency lexicon and, of course, different existing lexical resources), it was a challenge to adapt the annotation guidelines, the data model and the tools used for the original version. We describe the process and results of working in such a setup. We also show the next steps to adapt the annotation process, data structures and formats and tools necessary to make the addition of a new language in the future more smooth and efficient, and possibly to allow for various teams to work on SynSemClass extensions to many languages concurrently. We also present the latest release which contains the results of adding German, freely available for download as well as for online access.

LLOD (Linguistic Linked Open Data) is a generic name for a set of mutually connected language resources, using ontological relations. The connections between concepts and between concepts and their expressions in natural language make them suitable for both research and industrial applications in the area of content analysis, natural language understanding, (language- and knowledge-based) inferencing and other tasks. In the presented task, the concrete work will be on converting the SynSemClass project dataset (in part as a result of a previous Humane AI Net microproject called META-O-NLU) into LLOD, connecting it to the huge amount or interlinked data already available. A partner is involved in the Prêt-à-LLOD H2020 project, making this project synergistic in nature and multiplicative in terms of results in previous projects. Partners are also involved in the COST Action “European network for Web-centered linguistic data science” (NexusLinguarum).

This report presents a guideline for including German synonyms into the multilingual SynSemClass lexicon.

The 2020 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and languages. Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization; for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework. The task received submissions from eight teams, of which two do not participate in the official ranking because they arrived after the closing deadline or made use of additional training data. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://mrp.nlpl.eu

With the increasing number of platforms, grids and infrastructures in the wider area of Language Technologies (LT), NLP, NLU, speech, interaction and language-centric AI, there is also a growing need for sharing experiences, approaches and best practices to learn and benefit from the work of others and also, practically, to start a collaboration towards platform interoperability. The 1st International Workshop for Language Technology Platforms (IWLTP 2020) addresses all smaller and larger language grids, language-related infrastructures, platform initiatives as well as collaborative research projects that touch upon LT platforms, especially platform interoperability and related topics, both in Europe and world-wide. Its objective is to exchange and discuss observations, experiences, solutions, best practices as well as current and future challenges. The workshop also addresses the issue of fragmentation in the Language Technology landscape. Instead of “platform islands” that simply exist side by side, possibly even competing with each other, initiatives should discuss how their platforms can be made interoperable and how they can interact with one another to create synergies towards a productive LT platform ecosystem. A total of 30 papers were submitted to IWLTP 2020, 17 of which were accepted (acceptance rate: 56.7%). This proceedings volume contains all 17 accepted papers.

Discourse Representation Theory (DRT) is a formal account for representing the meaning of natural language discourse. Meaning in DRT is modeled via a Discourse Representation Structure (DRS), a meaning representation with a model-theoretic interpretation, which is usually depicted as nested boxes. In contrast, a directed labeled graph is a common data structure used to encode semantics of natural language texts. The paper describes the procedure of dressing up DRSs as directed labeled graphs to include DRT as a new framework in the 2020 shared task on Cross-Framework and Cross-Lingual Meaning Representation Parsing. Since one of the goals of the shared task is to encourage unified models for several semantic graph frameworks, the conversion procedure was biased towards making the DRT graph framework somewhat similar to other graph-based meaning representation frameworks., Comment: 10 pages, 4 figures, 4 tables, CoNLL 2020 Shared Task

Europe is a multilingual society with 24 EU Member State languages and dozens of additional languages including regional and minority languages and languages spoken by immigrants and trade partners. While languages are an essential part of our cultural heritage, language barriers continue to be unbreachable in many situations. The only option to enable and to benefit from multilingualism is through Language Technologies (LT) including Natural Language Processing (NLP) and Speech Technologies. The commercial European LT landscape is dominated by hundreds of SMEs. While the academic and industrial European LT community is world class, it is also massively fragmented. We describe the European Language Grid (ELG), Release 2. The system is targeted to evolve into the primary platform and marketplace for LT in Europe by providing one umbrella platform for the European LT landscape, including research and industry, enabling all stakeholders to showcase, share and distribute their services, products and resources. At the end of our EU-funded project, which will establish a legal entity in late 2021, the ELG will provide access to approx. 1300 commercial and non-commercial services for all European languages, as well as thousands of language resources (LRs). ELG will enable the whole European LT community to upload their technologies and resources and to deploy them through the ELG. The ELG is also meant to support digital language equality in Europe, i.e., to foster a situation in which all languages are supported through technologies equally well. We currently still have an extreme imbalance, characterised by a stark predominance of LTs for English.

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.

The AMR (Abstract Meaning Representation) formalism for representing meaning of natural language sentences puts emphasis on predicate-argument structure and was not designed to deal with scope and quantifiers. By extending AMR with indices for contexts and formulating constraints on these contexts, a formalism is derived that makes correct predictions for inferences involving negation and bound variables. The attractive core predicate-argument structure of AMR is preserved. The resulting framework is similar to the meaning representations of Discourse Representation Theory employed in the Parallel Meaning Bank.

Since 2016, the tour de CLARIN initiative has been periodically highlighting prominent user involvement activities in the CLARIN network in order to increase the visibility of its members, reveal the richness of the CLARIN landscape, and display the full range of activities that show what CLARIN has to offer to researchers, teachers, students, professionals and the general public interested in using and processing language data in various forms. the initiative was initially conceived as a series of blog posts published on the CLARIN webpage and disseminated through the CLARIN newsflash and social media channels. In 2018, the results of the initiative were published in a printed volume. Gradually, Tour de CLARIN has proven to be one of the flagship user involvement initiatives by CLARIN ERIC, is highly valuable for our network and incredibly popular with our readers. that is why the initiative has since been expanded from presentations of the work carried out by national consortia to also feature the work of CLARIN Knowledge Centres, which provide a physical or virtual place where researchers, educators and developers alike can get cross-border access to knowledge and expertise in specific areas. As a reflection of the double focus, this second volume of Tour de CLARIN is organized into two parts. In Part 1, we present the seven countries which have been featured since November 2018, when the first volume was published: Estonia, Latvia, Denmark, Italy, Slovenia, Hungary, and Bulgaria. In this part, each country is presented with five chapters: an introduction to the consortium, their members and their work; a description of one of their key resources; a presentation of an outstanding tool; an account of a successful event for the researchers and students in their network; and an interview with a renowned researcher from the digital humanities or social sciences who has successfully used the consortium’s infrastructure in their research. In Part 2, we present the work of the four Knowledge Centres that have been visited thus far: the Knowledge Centre for treebanking, the Knowledge Centre for the Languages of Sweden, the TalkBank Knowledge Centre, and the Czech Knowledge Centre for Corpus Linguistics. In this part, each K-centre is presented with two chapters: a presentation of what the K-centre offers to researchers; and an interview with either a renowned researcher who has collaborated with the K-centre or a leading developer who is part of the centre itself and who provided as with valuable insight into what the centre offers.

Optical Music Recognition (OMR) is the field of computationally reading music notation. This thesis presents, in the form of dissertation by publication, contributions to the theory, resources, and methods of OMR especially for handwritten notation. The main contributions are (1) the Music Notation Graph (MuNG) formalism for describing arbitrarily complex music notation using an oriented graph that can be unambiguously interpreted in terms of musical semantics, (2) the MUSCIMA++ dataset of musical manuscripts with MuNG as ground truth that can be used to train and evaluate OMR systems and subsystems from the image all the way to extracting the musical semantics encoded therein, and (3) a pipeline for performing OMR on musical manuscripts that relies on machine learning both for notation symbol detection and the notation assembly stage, and on properties of the inferred MuNG representation to deterministically extract the musical semantics. While the the OMR pipeline does not perform flawlessly, this is the first OMR system to perform at basic useful tasks over musical semantics extracted from handwritten music notation of arbitrary complexity.

Deep learning is bringing breakthroughs to many computer vision subfields including Optical Music Recognition (OMR), which has seen a series of improvements to musical symbol detection achieved by using generic deep learning models. However, so far, each such proposal has been based on a specific dataset and different evaluation criteria, which made it difficult to quantify the new deep learning-based state-of-the-art and assess the relative merits of these detection models on music scores. In this paper, a baseline for general detection of musical symbols with deep learning is presented. We consider three datasets of heterogeneous typology but with the same annotation format, three neural models of different nature, and establish their performance in terms of a common evaluation standard. The experimental results confirm that the direct music object detection with deep learning is indeed promising, but at the same time illustrates some of the domain-specific shortcomings of the general detectors. A qualitative comparison then suggests avenues for OMR improvement, based both on properties of the detection model and how the datasets are defined. To the best of our knowledge, this is the first time that competing music object detection systems from the machine learning paradigm are directly compared to each other. We hope that this work will serve as a reference to measure the progress of future developments of OMR in music object detection.

While studying verbal synonymy, we have investigated the relation between syntax and semantics in hope that the exploration of this relationship will help us to get more insight into the question of synonymy as the relationship relating (similar) meanings between different lexemes. Most synonym lexicons (Wordnets and similar thesauri) are based on an intuition about the similarity of word meanings, or on notions like “semantic roles.” In some cases, syntax is also taken into account, but we have found no annotation and/or evaluation experiment to see how strongly can syntax contribute to synonym specification. We have prepared an annotation experiment for which we have used two treebanks (Czech and English) from the Universal Dependencies (UD) set of parallel corpora (PUDs) in order to see how strong correlation exists between syntax and the assignment of verbs in context to pre-determined (bilingual) classes of synonyms. The resulting statistics confirmed that while syntax does support decisions about synonymy, such support is not strong enough and that more semantic criteria are indeed necessary. The results of the annotation will also help to further improve rules and specifications for creating synonymous classes. Moreover, we have collected evidence that the annotation setup that we have used can identify synonym classes to be merged, and the resulting data (which we plan to publish openly) can possibly serve for the evaluation of automatic methods used in this area.

Every year, 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 2018, one of two tasks was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on test input.

Chimera is a machine translation system that combines the TectoMT deep-linguistic core with phrase-based MT system Moses. For English–Czech pair it also uses the Depfix post-correction system. All the components run on Unix/Linux platform and are open source (available from Perl repository CPAN and the LINDAT/CLARIN repository). The main website is https://ufal.mff.cuni.cz/tectomt. The development is currently supported by the QTLeap 7th FP project (http://qtleap.eu).

This paper presents the work on Machine Translation (MT) that has been conducted within the KConnect project, funded under the H2020 programme and focused on development and commercialization of cloud-based services for multilingual Semantic Annotation, Semantic Search and Machine Translation of electronic health records and medical publications. We first present the main goal and role of MT in the project and then briefly describe the main methods and components developed in the project, including training data acquisition, methods of domain adaptation, deployment of MT as cloud-based web-service, and the training toolkit allowing easy adaptation of the MT service to new languages.

Meaning Representation is a newly developed formalism for representing meaning, which abstracts from syntax and some other phenomena but is (still) language-dependent. It has been developed by a consortium of mostly U.S. universities, with team members including Martha Palmer, Kevin Knight, Philipp Koehn, Ulf Hermjakob, Kathy McKeown, Nianwen Xue and others. In the talk, the basic facts about the AMR will be presented, and then comparison will be made for Czech and English as carried out in detail on a small 100-sentence corpus; some examples from Chinese-English comparison will also be shown. In addition, AMR will be compared to the deep syntactic representation used in the set of Prague Dependency Treebanks, and observations will be made about the level of abstraction used in these two formalisms. There is an ongoing work on possible conversion between the Prague deep syntactic representation and the AMR representation, and the main issues of such a conversion will also be described, as well as plans for future studies and possible corpus annotation work in the nearest future.

Machine Translation is as old as the field of Computational Linguistics itself. It is also a problem that has been predicted to „be solved in the next five years“ many times, but in fact it is not yet solved today. Machine translation has been naively considered a simple problem solvable by simple statistical means, then studied in depth by complicated but unsuccessful detailed sets of rules trying to describe all details of natural language use, only to return to statistical approach on a completely different level, using a combination of linguistic analysis and powerful machine learning algorithms.

The talk presented activities in the area of language technology and specifically machine translation in the medical domain in the context of EC-funded projects.

Fundamentals of Machine Translation technology using Deep language analysis using the traditional "Vauquois triangle" have been presented, which now use advanced statistical and machine learning techniques. In addition, latest WMT 2015 Shared Task competition results for the en-cs pair have been shown in which the Chimera system created at UFAL MFF UK won.

LINDAT/CLARIN, as a node of the pan-European research infrastructure Clarin ERIC, has been presented. Its repository has been featured together with data archivation techniques and related web services and applications.

This article provides an overview of the dissemination work carried out in META-NET from 2010 until early 2014; we describe its impact on the regional, national and international level, mainly with regard to politics and the situation of fundingfor langauge technology topics. This paper documents the initiative’s work throughout Europe in order to boost progress and innovation in our field.

We build a joint multimodal model of text and images for automatically assigning illustrative images to journalistic articles. We approach the task as an unsupervised representation learning problem of finding a common representation that abstracts from individual modalities, inspired by multimodal Deep Boltzmann Machine of Srivastava and Salakhutdinov. We use state-of-the-art image content classification features obtained from the Convolutional Neural Network of Krizhevsky et al. as input "images" and entire documents instead of keywords as input texts. A deep learning and experiment management library Safire has been developed. We have not been able to create a successful retrieval system because of difficulties with training neural networks on the very sparse word observation. However, we have gained substantial understanding of the nature of these difficulties and thus are confident that we will be able to improve in future work.

This article presents the results of one of the stages of the user-centered evaluation conducted in a framework of the EU project Khresmoi. In a controlled environment, users were asked to perform health-related tasks using a search engine specifically developed for trustworthy online health information. Twenty seven participants from largely the Czech Republic and France took part in the evaluation. All reported overall a positive experience, while some features caused some criticism. Learning points are summed up regarding running such types of evaluations with the general public and specifically with patients.