Torsten Zesch

Prof. Dr.-Ing. Torsten Zesch Foto: Henrik Schipper

Prof. Dr.-Ing. Torsten Zesch

Member of the Executive Board, Head of the research professorship "Computational Linguistics"

Email: torsten.zesch

Phone: +49 2331 987-4863

FernUniversität in Hagen
Universitätsstr. 27 - PRG / Buliding 5 / Room A125
58097 Hagen

What is my role in CATALPA?

As a computer scientist, I lead the research professorship Computational Linguistics and investigate with my team how language technology can support the educational process. I am a member of the CATALPA executive board.


The profitable integration of language technology methods in the educational process can only succeed in a joint research effort of different disciplines, which is realized in an ideal way in the research center.

    • Head of the W3 research professorship "Computational Linguistics" at the FernUniversität in Hagen (since March 2022)
    • President of the German Society for Computational Linguistics and Language Technology (GSCL) (since 2018)
    • W2 Professor “Sprachtechnologie”, Universität Duisburg-Essen (2020-2022)
    • W1 Professor “Sprachtechnologie”, Universität Duisburg-Essen (2014-2020)
    • Visiting Researcher, Educational Testing Service, Princeton, USA (2014)
    • Vertretungsprofessur (W2)“Knowledge Mining & Assessment”, Leibniz-Institut für Bildungsforschung und Bildungsinformation (DIPF), Frankfurt (2012)
    • Visiting Researcher, Bar-Ilan University, Ramat Gan, Israel (2012)
    • Dissertation (Dr.-Ing.), Computer Science, Technische Universität Darmstadt (2009)
    • Robust and efficient systems for processing language
    • Analysis of non-standard language and implicit structures
    • Application of language processing systems in the field of education
    • Bexte M, Horbach A, Zesch T (2021) Implicit Phenomena in Short-answer Scoring Data. In: Proceedings of the 1st Workshop on Understanding Implicit and Underspecified Language. pp 11–19
    • Wahlen A, Kuhn C, Zlatkin-Troitschanskaia O, C Gold, T Zesch (2020) Automated Scoring of Teachers’ Pedagogical Content Knowledge-A Comparison between Human and Machine Scoring. In: Frontiers in Education. p 149
    • Ding Y, Riordan B, Horbach A, Cahill A, Zesch T (2020) Don’t take “nswvtnvakgxpm” for an answer - The surprising vulnerability of automatic content scoring systems to adversarial input. In: Proceedings of the 28th International Conference on Computational Linguistics(COLING 2020)
    • Horbach A, Zesch T (2019) The Influence of Variance in Learner Answers on Automatic Content Scoring. In: Frontiers in Education. p 28
    • Zesch T, Horbach A (2018) ESCRITO-An NLP-Enhanced Educational Scoring Toolkit. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018)
    • Riordan B, Horbach A, Cahill A, Zesch T, Lee CM (2017) Investigating neural architectures for short answer scoring. In: Proceedings of the Building Educational Applications Workshop at EMNLP. Copenhagen, Denmark, p to appear
    • Pilán I, Volodina E, Zesch T (2016) Predicting proficiency levels in learner writings by transferring a linguistic complexity model from expert-written coursebooks. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. Osaka, Japan, pp 2101–2111
    • Zesch T, Heilman M, Cahill A (2015) Reducing Annotation Efforts in Supervised Short Answer Scoring. In: Proceedings of the Building Educational Applications Workshop at NAACL
    • Beinborn L, Zesch T, Gurevych I (2014) Predicting the Difficulty of Language Proficiency Tests. Trans Assoc Comput Linguist 2:517–529
    • Zesch T, Melamud O (2014) Automatic Generation of Challenging Distractors Using Context-Sensitive Inference Rules. In: Proceedings of the 9th Workshop on Innovative Use of NLP for Building Educational Applications at ACL. Baltimore, USA
    • Zesch T (2013) Detecting Malapropisms Using Measures of Contextual Fitness. Spec Issue TAL J “Managing Noise Signal Error Handl Nat Lang Process 53:11–31
    • Levy O, Zesch T, Dagan I, Gurevych I (2013) Recognizing Partial Textual Entailment. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL), Sofia, Bulgaria, pp 451–455
    • Zesch T (2012) Measuring Contextual Fitness Using Error Contexts Extracted from the Wikipedia Revision History. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012). Avignon, France, pp 529–538