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Karolina Zaczynska

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Evaluating German Transformer Language Models with Syntactic Agreement Tests

Jul 07, 2020
Karolina Zaczynska, Nils Feldhus, Robert Schwarzenberg, Aleksandra Gabryszak, Sebastian Möller

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Pre-trained transformer language models (TLMs) have recently refashioned natural language processing (NLP): Most state-of-the-art NLP models now operate on top of TLMs to benefit from contextualization and knowledge induction. To explain their success, the scientific community conducted numerous analyses. Besides other methods, syntactic agreement tests were utilized to analyse TLMs. Most of the studies were conducted for the English language, however. In this work, we analyse German TLMs. To this end, we design numerous agreement tasks, some of which consider peculiarities of the German language. Our experimental results show that state-of-the-art German TLMs generally perform well on agreement tasks, but we also identify and discuss syntactic structures that push them to their limits.

* Proceedings of the 5th Swiss Text Analytics Conference and the 16th Conference on Natural Language Processing, SwissText/KONVENS 2020, Zurich, Switzerland, June 23-25, 2020 [online only]. CEUR Workshop Proceedings 2624  
* SwissText + KONVENS 2020 
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QURATOR: Innovative Technologies for Content and Data Curation

Apr 25, 2020
Georg Rehm, Peter Bourgonje, Stefanie Hegele, Florian Kintzel, Julián Moreno Schneider, Malte Ostendorff, Karolina Zaczynska, Armin Berger, Stefan Grill, Sören Räuchle, Jens Rauenbusch, Lisa Rutenburg, André Schmidt, Mikka Wild, Henry Hoffmann, Julian Fink, Sarah Schulz, Jurica Seva, Joachim Quantz, Joachim Böttger, Josefine Matthey, Rolf Fricke, Jan Thomsen, Adrian Paschke, Jamal Al Qundus, Thomas Hoppe, Naouel Karam, Frauke Weichhardt, Christian Fillies, Clemens Neudecker, Mike Gerber, Kai Labusch, Vahid Rezanezhad, Robin Schaefer, David Zellhöfer, Daniel Siewert, Patrick Bunk, Lydia Pintscher, Elena Aleynikova, Franziska Heine

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In all domains and sectors, the demand for intelligent systems to support the processing and generation of digital content is rapidly increasing. The availability of vast amounts of content and the pressure to publish new content quickly and in rapid succession requires faster, more efficient and smarter processing and generation methods. With a consortium of ten partners from research and industry and a broad range of expertise in AI, Machine Learning and Language Technologies, the QURATOR project, funded by the German Federal Ministry of Education and Research, develops a sustainable and innovative technology platform that provides services to support knowledge workers in various industries to address the challenges they face when curating digital content. The project's vision and ambition is to establish an ecosystem for content curation technologies that significantly pushes the current state of the art and transforms its region, the metropolitan area Berlin-Brandenburg, into a global centre of excellence for curation technologies.

* Proceedings of QURATOR 2020: The conference for intelligent content solutions, Berlin, Germany, February 2020 
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Towards Discourse Parsing-inspired Semantic Storytelling

Apr 25, 2020
Georg Rehm, Karolina Zaczynska, Julián Moreno-Schneider, Malte Ostendorff, Peter Bourgonje, Maria Berger, Jens Rauenbusch, André Schmidt, Mikka Wild

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Previous work of ours on Semantic Storytelling uses text analytics procedures including Named Entity Recognition and Event Detection. In this paper, we outline our longer-term vision on Semantic Storytelling and describe the current conceptual and technical approach. In the project that drives our research we develop AI-based technologies that are verified by partners from industry. One long-term goal is the development of an approach for Semantic Storytelling that has broad coverage and that is, furthermore, robust. We provide first results on experiments that involve discourse parsing, applied to a concrete use case, "Explore the Neighbourhood!", which is based on a semi-automatically collected data set with documents about noteworthy people in one of Berlin's districts. Though automatically obtaining annotations for coherence relations from plain text is a non-trivial challenge, our preliminary results are promising. We envision our approach to be combined with additional features (NER, coreference resolution, knowledge graphs

* Proceedings of QURATOR 2020: The conference for intelligent content solutions, Berlin, Germany, February 2020 
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