11 Papers bei LREC 2022 angenommen

11 Papers unserer Fachrichtung wurden bei  LREC 2022 angenommen. Wir gratulieren allen Autorinnen und Autoren!

• Abstract Meaning Representation for Gesture. Richard Brutti, Lucia Donatelli, Kenneth Lai, James Pustejovsky. LREC 2022.
• Logic-Guided Message Generation from Raw Real-Time Sensor Data. Ernie Chang, Alisa Kovtunova, Stefan Borgwardt, Vera Demberg, Kathryn Chapman, Hui-Syuan Yeh. LREC 2022.
• Tracing Syntactic Change in the Scientific Genre: Two Universal Dependency-parsed Diachronic Corpora of Scientific English and German. Marie-Pauline Krielke, Luigi Talamo, Mahmoud Fawzi, Jörg Knappen. LREC 2022.
• DiHuTra: a Parallel Corpus to Analyse Differences between Human Translations. Ekaterina Lapshinova-Koltunski, Maja Popović and Maarit Koponen. LREC 2022.
• ParCorFull2.0: a Parallel Corpus Annotated with Full Coreference. Ekaterina Lapshinova-Koltunski, Pedro Augusto Ferreira, Elina Lartaud, Christian Hardmeier. LREC 2022.
• A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis. Shamsuddeen Hassan Muhammad, David Ifeoluwa Adelani, Sebastian Ruder, Ibrahim Said Ahmad, Idris Abdulmumin, Bello Shehu Bello, Monojit Choudhury, Chris Chinenye Emezue, Saheed Salahudeen Abdullahi, Anuoluwapo Aremu, Alipio Jeorge, Pavel Brazdil. LREC 2022.
• EPIC UdS - Creation and Applications of a Simultaneous Interpreting Corpus. Heike Przybyl, Ekaterina Lapshinova-Koltunski, Katrin Menzel, Stefan Fischer, Elke Teich. LREC 2022. SFB 1102.
• DiscoGeM: A Crowdsourced Corpus of Genre-Mixed Implicit Discourse Relations. Merel Scholman, Tianai Dong, Frances Yung, Vera Demberg. LREC 2022.
• Design Choices in Crowdsourcing Discourse Relation Annotations: The Effect of Worker Selection and Training. Merel Scholman, Valentina Pyatkin, Frances Yung, Ido Dagan, Reut Tsarfaty, Vera Demberg. LREC 2022.
• Modeling the Impact of Syntactic Distance and Surprisal on Cross-Slavic Text Comprehension. Irina Stenger, Philip Georgis, Tania Avgustinova, Bernd Möbius, Dietrich Klakow. LREC 2022.
• Barch: an English Dataset of Bar Chart Summaries. Iza Škrjanec, Muhammad Salman Edhi, Vera Demberg. LREC2022.