multimodal language data

Prof. Dr. Torsten Zesch

Giving feedback on free-text answers (in the form of grades or helpful hints) is a core educational task. Despite a large body of NLP research on the topic, assisting teachers with this task remains challenging. In this talk, we outline the linguistic and external factors influencing the performance level that NLP methods may reach for a given question. However, even in settings where automatic performance rivals humans, there are various practical requirements often overlooked in research that hinder adoption in the classroom and beyond.

Torsten Zesch a full professor of Computational Linguistics at CATALPA (Center of Advanced Technology for Assisted Learning and Predictive Analytics), FernUniversität in Hagen, Germany. He holds a doctoral degree in computer science from Technische Universität Darmstadt and was the president of the German Society for Computational Linguistics and Language Technology (GSCL) from 2017 to 2023. His main research interests are in educational natural language processing, in particular the ways in which teaching and learning processes can be supported by language technology. For this purpose, he develops methods for the automatic analysis of textual and multimodal language data, with a focus on robust and explainable models.

Institutions

• UHH, FB Informatik