Yuning Ding

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Yuning Ding (MSc) Photo: Henrik Schipper

Yuning Ding (MSc)

PhD Student in Junior Research Group “EduNLP”

Email: yuning.ding

Universitätsstr. 27 – PRG / Building 5
Room A 107 (1st floor)
58097 Hagen

What is my role within CATALPA?

As a Natural Language Processing (NLP) researcher, I am working on automatic essay scoring and generation of formative feedback to learners and summative feedback to teachers.

Why CATALPA?

The diverse composition of CATALPA gives me the opportunity to collaborate with scholars in different fields of study. In addition, CATALPA has a great sharing culture: through events like project presentations and colloquiums, I can learn about the latest working progress of my colleagues, increase my knowledge in related fields and get helpful feedback from other disciplines.

    • Member of the junior research group "Educational Natural Language Processing" at the Research Center CATALPA (Center of Advanced Technology for Assisted Learning and Predictive Analytics), FernUniversität in Hagen since January 2022.
    • Java Software Engineer, IT.NRW, Düsseldorf (11. 2020 - 12. 2021)
    • Research assistant at Language Technology Lab led by Professor Torsten Zesch, University of Duisburg-Essen (11. 2019 - 10. 2020)
    • M.Sc. in Applied Cognitive and Media Science with Specialization in Cognition & Artificial Intelligence at University of Duisburg-Essen (10. 2017 - 09.2019)
    • B.Sc. in Applied Cognitive and Media Science at University of Duisburg-Essen (10. 2014 - 08. 2017)
    • B.A. in Communications at University of International Relations, Beijing (09. 2009 – 07. 2013)

  • My research interests are Natural Language Processing for educational applications including automatic essay scoring and feedback generation. I am motivated by the vision that using Artificial intelligence to help students write better.

    • EduNLP

  • 2024

    Journals

    • Schaller, N.-J., Horbach, A., Höft, L. I., Ding, Y., Bahr, J. L., Meyer, J., & Jansen, T. (2024). DARIUS: A comprehensive learner corpus for argument mining in german-language essays.

    Conferences

    • Ding, Y., Kashefi, O., Somasundaran, S., & Horbach, A. (2024). When argumentation meets cohesion: Enhancing automatic feedback in student writing. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), 17513–17524.

    Talks and Poster Presentations

    • Wehrhahn, F., Ding, Y., Gaschler, R., Zhao, F., & Horbach, A. (2024, June 26–28). Argumentative essay writing practice with automated feedback and highlighting. [Poster Presentation]. EARLI SIG WRITING 2024 – ways2write, Université Paris Nanterre, France.

    2023

    Journals

    • Horbach, A., Pehlke, J., Laarmann-Quante, R., & Ding, Y. (2023). Crosslingual content scoring in five languages using machine-translation and multilingual transformer models. International Journal of Artificial Intelligence in Education, 1–27.

    Conferences

    • Ding, Y., Bexte, M., & Horbach, A. (2023a). CATALPA_EduNLP at PragTag-2023. In M. Alshomary, C.-C. Chen, S. Muresan, J. Park, & J. Romberg (Eds.), Proceedings of the 10th workshop on argument mining (pp. 197–201). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.argmining-1.22
    • Ding, Y., Bexte, M., & Horbach, A. (2023b). Score it all together: A multi-task learning study on automatic scoring of argumentative essays. Findings of the Association for Computational Linguistics: ACL 2023, 13052–13063. https://aclanthology.org/2023.findings-acl.825
    • Ding, Y., Trüb, R., Fleckenstein, J., Keller, S., & Horbach, A. (2023). Sequence tagging in EFL email texts as feedback for language learners. Proceedings of the 12th Workshop on NLP for Computer Assisted Language Learning, 53–62.

    2022

    Conferences

    • Ding, Y., Bexte, M., & Horbach, A. (2022). Don’t drop the topic - the role of the prompt in argument identification in student writing. Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022), 124–133. https://aclanthology.org/2022.bea-1.17

    2020

    Conferences

    • Ding, Y., Horbach, A., Wang, H., Song, X., & Zesch, T. (2020). Chinese Content Scoring: Open-Access Datasets and Features on Different Segmentation Levels. Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing(AACL-IJCNLP 2020). https://www.aclweb.org/anthology/2020.aacl-main.37.pdf
    • 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. Proceedings of the 28th International Conference on Computational Linguistics(COLING 2020). https://www.aclweb.org/anthology/2020.coling-main.76.pdf

    2017

    Conferences

    • Horbach, A., Ding, Y., & Zesch, T. (2017). The Influence of Spelling Error on Content Scoring Performance. Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications, 45–53. http://www.aclweb.org/anthology/W17-5908
    • Horbach, A., Scholten-Akoun, D., Ding, Y., & Zesch, T. (2017). Fine-grained essay scoring of a complex writing task for native speakers. Proceedings of the Building Educational Applications Workshop at EMNLP, 357–366. http://aclweb.org/anthology/W17-5040

Research Center CATALPA | 02.07.2024