Anne Lauscher

Dr. Anne Lauscher is a Researcher at the Data and Web Science Group, University of Mannheim, School of Business Informatics and Mathematics. Her work focuses on neural methods for natural language understanding, particularly in Argument Mining, Scientific Publication Mining (scitorics), and ethical considerations in NLP. She investigates language representations, bias detection in models, and fair AI. She has been involved in projects like the Linked Open Citation Database (LOC-DB) and contributes to conferences like ACL and EMNLP. She has taught courses on Text Analytics, Machine Learning, and Ethics in NLP, and supervised theses on topics like detecting unfairness in Arabic text representations and hyperpartisan news detection. Her research bridges computational linguistics with societal ethics, aiming to improve fairness and interpretability in AI systems.

Her research interests span Argumentation Mining, Scientific Publication Mining, scitorics, Transfer Learning, Representation Learning, and Ethics in NLP. She explores how to inject knowledge into language models and analyze biases in text representations. Her work on 'scitorics' involves analyzing rhetorical aspects of scientific writing to understand argumentation structures.

Publications highlight contributions to bias mitigation (e.g., DebIE platform), specialized language models, and multilingual datasets like Multi2WOZ. Her work appears in top venues such as EMNLP, ACL, and COLING. She actively contributes to the academic community through roles like Publication Co-Chair for EurNLP 2020 and PC member for multiple conferences.

Her advising includes guiding students on thesis projects addressing fairness, argumentation, and NLP applications. She collaborates with institutions like Stuttgart Media University and the Data and Web Science Group to advance research in ethical AI and NLP.