Ji-Ung Lee

Ji-Ung Lee M.Sc.

Doctoral researcher

+49 6151 16-25293
+49 6151 16-25295

Hochschulstraße 10
64289 Darmstadt

Office: S2|02 B113

More information

About me

I am a third year PhD student at the UKP Lab, TU Darmstadt. My research revolves around methods for effective model training. This is usually coupled with low-data scenarios and often involves interacting with a user who can provide the labels for queried instances. If you are interested in this research topic feel free to drop me a message!


Research interests

  • Machine Learning
  • Active Learning
  • Language and Domain Adaptation
  • Computer-assisted Language Learning
  • Few Shot Learning

Reviewing activities

ACL 2019, BEA 2019, EMNLP 2019, ACL 2020 (outstanding reviewer), BEA 2020, EMNLP 2020



I actively participate in teaching activities around our lab. I am teaching or have been teaching following courses:

  • Bachelor Praktikum (Winter Term 2020/ 2021)
  • Bachelor Praktikum (Winter Term 2019/ 2020)
  • Text Analytics (Summer Term 2018)

Thesis supervision

Former students:

  • Erik Schwan (B.Sc. thesis)
  • Thorsten Hollstein (M.Sc. thesis)
  • Igor Cherepanov (M.Sc. thesis)
  • Darjush Siadohoni (M.Sc. thesis)
  • Jonathan Gruhle (B.Sc. thesis)

If you are interested in a thesis, please have a look at this task description.

Biographical information


  • 2017: M.Sc. in Computer Science at Technische Universität Darmstadt
    • Thesis: Automated Annotation of Argumentation Components for eCommerce
    • Secondary Subject: Biological Psychology
  • 2013: B.Sc. in Computer Science at Technische Universität Darmstadt
    • Thesis: Transductive Pairwise Classification


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Gruppiere nach: Publikationsjahr | Typ des Eintrags | Keine Gruppierung
Springe zu: 2020 | 2019 | 2018 | 2017
Anzahl der Einträge: 6.


Müller, Marvin ; Frick, Nicholas ; Lee, Ji-Ung ; Metternich, Joachim ; Gurevych, Iryna (2020):
Chats als Datengrundlage für KI-Anwendungen in der Produktion.
In: Zeitschrift für Wirtschaftlichen Fabrikbetrieb : ZWF, (7-8), 115. Carl Hanser Verlag, S. 520-523, ISSN 0947-0085,
DOI: 10.3139/104.112360,

Lee, Ji-Ung ; Meyer, Christian M. ; Gurevych, Iryna (2020):
Empowering Active Learning to Jointly Optimize System and User Demands.
In: The 58th annual meeting of the Association for Computational Linguistics (ACL 2020), virtual Conference, 05.-10.07.2020, S. 4233-4247, [Online-Edition: https://www.aclweb.org/anthology/2020.acl-main.390/],


Lee, Ji-Ung ; Schwan, Erik ; Meyer, Christian M. (2019):
Manipulating the Difficulty of C-Tests.
In: The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy, 28.07.2019-02.08.2019, S. 360-370, [Online-Edition: https://www.aclweb.org/anthology/P19-1035],

Eger, Steffen ; Şahin, Gözde Gül ; Rücklé, Andreas ; Lee, Ji-Ung ; Schulz, Claudia ; Mesgar, Mohsen ; Swarnkar, Krishnkant ; Simpson, Edwin ; Gurevych, Iryna (2019):
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems.
In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, Minneapolis, USA, In: The 2019 Conference of the North American Chapter of the Association for Computational Linguistics, Minneapolis, USA, 02.06.2019--07.10.2019, S. 1634-1647, [Online-Edition: https://www.aclweb.org/anthology/N19-1165],


Lee, Ji-Ung ; Meyer, Christian M. ; Gurevych, Iryna (2018):
Avoid playing learner and system off against each other.
In: Abstracts of the Joint Meeting of WG3 & WG5 "Motivational, ethical and legal issues in crowdsourcing" of the European Network for Combining Language Learning with Crowdsourcing Techniques, In: enetCollect - Joint WG3 & WG5 Meeting, Leiden, Netherlands, 24-25 October 2018, [Online-Edition: https://fileserver.ukp.informatik.tu-darmstadt.de/UKP_Webpag...],


Lee, Ji-Ung ; Eger, Steffen ; Daxenberger, Johannes ; Gurevych, Iryna (2017):
UKP TU-DA at GermEval 2017: Deep Learning for Aspect Based Sentiment Detection.
In: Proceedings of the GermEval 2017 – Shared Task on Aspect-based Sentiment in Social Media Customer Feedback, Berlin, Germany, S. 22-29, [Online-Edition: https://download.hrz.tu-darmstadt.de/media/FB20/Dekanat/Publ...],

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