Wordnets have been a popular lexical resource type for many years. Their sense-based representation of lexical items and numerous relation structures have been used for a variety of computational and linguistic applications. The inclusion of different wordnets into multilingual wordnet networks has further extended their use into the realm of cross-lingual research. Wordnets have been released for many spoken languages. Research has also been carried out into the creation of wordnets for several sign languages, but none have yet resulted in publicly available datasets. This article presents our own efforts towards an inclusion of sign languages in a multilingual wordnet, starting with Greek Sign Language (GSL) and German Sign Language (DGS). Based on differences in available language resources between GSL and DGS, we trial two workflows with different coverage priorities. We also explore how synergies between both workflows can be leveraged and how future work on additional sign languages could profit from building on existing sign language wordnet data. The results of our work are made publicly available.

One of the challenges that sign language researchers face is the identification of suitable language datasets, particularly for cross-lingual studies. There is no single source of information on what sign language corpora and lexical resources exist or how they compare. Instead, they have to be found through extensive literature review or word-of-mouth. The amount of information available on individual datasets can also vary widely and may be distributed across different publications, data repositories and (potentially defunct) project websites. This article introduces the Sign Language Dataset Compendium, an extensive overview of linguistic resources for sign languages. It covers existing corpora and lexical resources, as well as commonly used data collection tasks. Special attention is paid to covering resources for many different languages from around the globe. All information is provided in a standardised format to make entries comparable, but kept flexible enough to allow for differences in content. The compendium is intended as a growing resource that will be updated regularly.

For developing sign language technologies like automatic translation, huge amounts of training data are required. Even the larger corpora available for some sign languages are tiny compared to the amounts of data used for corresponding spoken language technologies. The overarching goal of the European project EASIER is to develop a framework for bidirectional automatic translation between sign and spoken languages and between sign languages. One part of this multi-dimensional project is that it will pool available language resources from European sign languages into a larger dataset to address the data scarcity problem. This approach promises to open the floor for lower-resourced sign languages in Europe. This article focusses on efforts in the EASIER project to allow for new languages to make use of such technologies in the future. What are the characteristics of sign language resources needed to train recognition, translation, and synthesis algorithms, and how can other countries including those without any sign resources follow along with these developments? The efforts undertaken in EASIER include creating workflow documents and organizing training sessions in online workshops. They reflect the current state of the art, and will likely need to be updated in the coming decade.

The publication of resources for minority languages requires a balance between making data open and accessible and respecting the rights and needs of its language community. The FAIR principles were introduced as a guide to good open data practices and they have since been complemented by the CARE principles for indigenous data governance. This article describes how the DGS Corpus implemented these principles and how the two sets of principles affected each other. The DGS Corpus is a large collection of recordings of members of the deaf community in Germany communicating in their primary language, German Sign Language (DGS); it was created to be both as a resource for linguistic research and as a record of the life experiences of deaf people in Germany. The corpus was designed with CARE in mind to respect and empower the language community and FAIR data publishing was used to enhance its usefulness as a scientific resource.

Development of automatic translation between signed and spoken languages has lagged behind the development of automatic translation between spoken languages, but it is a common misperception that extending machine translation techniques to include signed languages should be a straightforward process. A contributing factor is the lack of an acceptable method for displaying sign language apart from interpreters on video. This position paper examines the challenges of displaying a signed language as a target in automatic translation, analyses the underlying causes and suggests strategies to develop display technologies that are acceptable to sign language communities.

In 2018 the DGS-Korpus project published the first full release of the Public DGS Corpus. This event marked a change of focus for the project. While before most attention had been on increasing the size of the corpus, now an increase in its depth became the priority. New data formats were added, corpus annotation conventions were released and OpenPose pose information was published for all transcripts. The community and research portal websites of the corpus also received upgrades, including persistent identifiers, archival copies of previous releases and improvements to their usability on mobile devices. The research portal was enhanced even further, improving its transcript web viewer, adding a KWIC concordance view, introducing cross-references to other linguistic resources of DGS and making its entire interface available in German in addition to English. This article provides an overview of these changes, chronicling the evolution of the Public DGS Corpus from its first release in 2018, through its second release in 2019 until its third release in 2020.

This paper presents SignHunter, a tool for collecting isolated signs, and discusses application possibilities. SignHunter is successfully used within the DGS-Korpus project to collect name signs for places and cities. The data adds to the content of a German Sign Language (DGS) – German dictionary which is currently being developed, as well as a freely accessible subset of the DGS Corpus, the Public DGS Corpus. We discuss reasons to complement a natural language corpus by eliciting concepts without context and present an application example of SignHunter.

The Public DGS Corpus is published in two different formats, that is subtitled videos for lay persons and lemmatized and annotated transcripts and videos for experts. In addition, a draft version with the first set of preliminary entries of the DGS dictionary (DW-DGS) to be completed in 2023 is now online. The Public DGS Corpus and the DW-DGS are conceived of as stand-alone products, but are nevertheless closely interconnected to offer additional and complementary informative functions. In this paper we focus on linking the published products in order to provide users access to corpus and corpus-based dictionary in various, interrelated ways. We discuss which links are thought to be useful and what challenges the linking of the products poses. In addition we address the inclusion of links to other, older lexical resources (LSP dictionaries).

For publishing sign language corpus data on the web, anonymization is crucial even if it is impossible to hide the visual appearance of the signers: In a small community, even vague references to third persons may be enough to identify those persons. In the case of the DGS Korpus (German Sign Language corpus) project, we want to publish data as a contribution to the cultural heritage of the sign language community while annotation of the data is still ongoing. This poses the question how well anonymization can be achieved given that no full linguistic analysis of the data is available. Basically, we combine analysis of all data that we have, including named entity recognition on translations into German. For this, we use the WebLicht language technology infrastructure. We report on the reliability of these methods in this special context and also illustrate how the anonymization of the video data is technically achieved in order to minimally disturb the viewer.