This paper presents an overview of AVASAG; an ongoing applied-research project developing a text-to-sign-language translation system for public services. We describe the scientific innovation points (geometry-based SL-description, 3D animation and video corpus, simplified annotation scheme, motion capture strategy) and the overall translation pipeline.

Data is key in training modern language technologies. In this paper, we summarise the findings of the first pan-European study on obstacles to sharing language data across 29 EU Member States and CEF-affiliated countries carried out under the ELRC White Paper action on Sustainable Language Data Sharing to Support Language Equality in Multilingual Europe. Why Language Data Matters. We present the methodology of the study, the obstacles identified and report on recommendations on how to overcome those. The obstacles are classified into (1) lack of appreciation of the value of language data, (2) structural challenges, (3) disposition towards CAT tools and lack of digital skills, (4) inadequate language data management practices, (5) limited access to outsourced translations, and (6) legal concerns. Recommendations are grouped into addressing the European/national policy level, and the organisational/institutional level.

Web debates play an important role in enabling broad participation of constituencies in social, political and economic decision-taking. However, it is challenging to organize, structure, and navigate a vast number of diverse argumentations and comments collected from many participants over a long time period. In this paper we demonstrate Common Round, a next generation platform for large-scale web debates, which provides functions for eliciting the semantic content and structures from the contributions of participants. In particular, Common Round applies language technologies for the extraction of semantic essence from textual input, aggregation of the formulated opinions and arguments. The platform also provides a cross-lingual access to debates using machine translation.

Foreign name expressions written in Chinese characters are difficult to recognize since the sequence of characters represents the Chinese pronunciation of the name. This paper suggests that known English or German person names can reliably be identified on the basis of the similarity between the Chinese and the foreign pronunciation. In addition to locating a person name in the text and learning that it is foreign, the corresponding foreign name is identified, thus gaining precious additional information for cross-lingual applications. This idea is implemented as a statistical module into the rule-based shallow parsing system SProUT, forming the HyFex system. The statistical component is invoked if a sequence of “trigger” characters is found that may correspond to a foreign name. Their phonetic Pinyin representation is produced and compared to the phonetic representations (SAMPA) of given foreign names, which are generated by the MARY TTS system for German and English pronunciations. This comparison is achieved by a hand-crafted metric that assigns costs to specific edit operations. The person name corresponding to the SAMPA representation with the lowest costs attached is returned as the most similar result, if a threshold is not exceeded. Our evaluation on publicly available data shows competitive results.

Official travel warnings published regularly in the internet by the ministries for foreign affairs of France, Germany, and the UK provide a useful resource for assessing the risks associated with travelling to some countries. The shallow IE system SProUT has been extended to meet the specific needs of delivering a language-neutral output for English, French, or German input texts. A shared type hierarchy, a feature-enhanced gazetteer resource, and generic techniques of merging chunk analyses into larger results are major reusable results of this work.