Anne Lauscher

Researcher

B6, 26, Room C 1.05

Email: anne (at) informatik.uni-mannheim.de

Focus group: Natural language processing

About

I am a Ph.D. candidate under the supervision of Prof. Simone Paolo Ponzetto and Prof. Goran Glavaš working on neural methods for natural language understanding. My research centers around two main topics: Argument Mining and Language Representations. In Argument Mining, the overall goal is to teach machines to understand human argumentation. This involves several challenging tasks such as identifying argumentative units (e.g., claims, premises) and relationships (e.g., attack, support) and predicting the argumentative quality of texts. Given that we can understand scientific publications as arguments, I also work on solving various problems in the area of scientific publication mining (with Prof. Eckert (Stuttgart Media University)). In this context, I am trying to obtain a deeper insight into the argumentative nature of scientific writing by developing state-of-the-art methods for analyzing and exploring the rhetorical aspects of scientific publications, which we collectively dub 'scitorics'.

The key to being successful in computationally analyzing argumentation is employing high-quality language representations. To this end, I also work on the specialization of language representations, for instance, by injecting linguistic or common sense knowledge into text encoder models. In this context, I am also trying to understand how ethically fair text representations should look like, for example, by detecting unfair biases such as sexism and racism in our models and by developing debiasing algorithms.

Formerly, I was funded by the project “Linked Open Citation Database (LOC-DB). You can find more information about the project here: https://locdb.bib.uni-mannheim.de.

Research Interests

Argumentation Mining
Scientific Publication Mining and Scitorics
Transfer Learning
Representation Learning
Ethics and Natural Language Processing

Teaching

Teaching Assistant:

Text Analytics (HWS2019)
Introduction to Machine Learning for GESS (HWS2019)
Introduction to Data Science (HWS2018, HWS2019)
Ethics in Natural Language Processing (HWS2018, seminar)
Semantic Relation Extraction and Classification in Scientific Papers (HWS2017, team project)
Information Retrieval and Web Search (FSS2017)

Lecturer:

Business Informatics for WiPäds (FSS2019)
Information Management (DHBW Mannheim: AGW13, AGW14, WGW15, WGW16, WGW17)

Thesis Supervisor:

“Detecting Unfairness in Arabic Text Representations”
“Hyperpartisan News Detection”
“MinScIE: Citation-centered Open Information Extraction”
“Multi-task Learning for Rumour Stance Classification and Veracity Prediction of Tweets”

Community services

Publication Co-Chair for EurNLP 2020
PC Member for SDP 2020
Reviewer for ACL 2020 (Ethics and NLP Track)
PC Member TextGraphs 2018, 2019
PC Member of ESSP 2019
Sub-reviewer for AAAI 2018
Reviewer for BIRNDL 2017

Lutz, M., Choenni, R., Strohmaier, M. and Lauscher, A. (2024). Local contrastive editing of gender stereotypes. In , EMNLP 2024 : the 2024 Conference on Empirical Methods in Natural Language Processing : proceedings of the conference : November 12–16, 2024 (S. 21474-21493). , Association for Computational Linguistics: Kerrville.
Hung, C.-C., Lauscher, A., Hovy, D., Ponzetto, S. P. and Glavaš, G. (2023). Can demographic factors improve text classification? Revisiting demographic adaptation in the age of transformers. In , The 17th Conference of the European Chapter of the Association for Computational Linguistics : Findings of EACL 2023, May 2–6, 2023 (S. 1565-1580). , Association for Computational Linguistics (ACL): Stroudsburg, PA.
Holtermann, C., Lauscher, A. and Ponzetto, S. P. (2022). Fair and argumentative language modeling for computational argumentation. In , The 60th Annual Meeting of the Association for Computational Linguistics : proceedings of the conference, May 22–27, 2022 (S. 7841-7861). , Association for Computational Linguistics (ACL): Stroudsburg, PA.
Hung, C.-C., Lauscher, A., Ponzetto, S. P. and Glavaš, G. (2022). DS-TOD: Efficient domain specialization for task-oriented dialog. In , The 60th Annual Meeting of the Association for Computational Linguistics, Findings of ACL 2022 : May 22–27, 2022 (S. 891–904). , Association for Computational Linguistics (ACL): Stroudsburg, PA.
Hung, C.-C., Lauscher, A., Vulić, I., Ponzetto, S. P. and Glavaš, G. (2022). Multi2WOZ: A robust multilingual dataset and conversational pretraining for task-oriented dialog. In , The 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies : proceedings of the conference, July 10–15, 2022 (S. 3687-3703). , Association for Computational Linguistics (ACL): Stroudsburg, PA.
Friedrich, N., Lauscher, A., Ponzetto, S. P. and Glavaš, G. (2021). DebIE: A platform for implicit and explicit debiasing of word embedding spaces. In , The 16th Conference of the European Chapter of the Association for Computational Linguistics : proceedings of the System Demonstrations, April 19–23, 2021 (S. 91–98). , Association for Computational Linguistics (ACL): Stroudsburg, PA.
Walter, T., Kirschner, C., Eger, S., Glavaš, G., Lauscher, A. and Ponzetto, S. P. (2021). Diachronic analysis of German parliamentary proceedings: Ideological shifts through the lens of political biases. In , 2021 ACM/IEEE Joint Conference on Digital Libraries : JCDL 2021, virtual conference, hosted by the University of Illinois at Urbana-Champaign, USA, 27–30 September 2021, proceedings (S. 51–60). , IEEE: Piscataway, NJ.
Yamamoto, S., Lauscher, A., Ponzetto, S. P., Glavaš, G. and Morishima, S. (2021). Self-supervised learning for visual summary identification in scientific publications. In , BIR 2021, Proceedings of the 11th International Workshop on Bibliometric-Enhanced Information Retrieval, co-located with 43rd European Conference on Information Retrieval (ECIR 2021), Lucca, Italy (online only), April 1st, 2021 (S. 5–19). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Daquino, M., Peroni, S., Shotton, D., Colavizza, G., Ghavimi, B., Lauscher, A., Mayr, P., Romanello, M. and Zumstein, P. (2020). The OpenCitations Data Model. In , The Semantic Web – ISWC 2020 : 19th International Semantic Web Conference, Athens, Greece, November 2–6, 2020, Proceedings, Part II (S. 447–463). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Lauscher, A., Glavaš, G., Ponzetto, S. P. and Vulić, I. (2020). A general framework for implicit and explicit debiasing of distributional word vector spaces. In , The Thirty-Fourth AAAI Conference on Artificial Intelligence, the Thirty-Second Innovative Applications of Artificial Intelligence Conference, the Tenth AAAI Symposium on Educational Advances in Artificial Intelligence : New York, NY, February 7–12, 2020 (S. 8131-8138). , AAAI Press: Palo Alto, CA.
Lauscher, A., Majewska, O., Ribeiro, L. F. R., Gurevych, I., Rozanov, N. and Glavaš, G. (2020). Common sense or world knowledge? Investigating adapter-based knowledge injection into pretrained transformers. In , Deep Learning Inside Out (DeeLIO): the First Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, proceedings of the workshop : November 19 2020, co-located with EMNLP 2020 (S. 43–49). , Association for Computational Linguistics: Stroudsburg, PA.
Lauscher, A., Ravishankar, V., Vulić, I. and Glavaš, G. (2020). From zero to hero: On the limitations of zero-shot language transfer with multilingual transformers. In , 2020 Conference on Empirical Methods in Natural Language Processing, proceedings of the conference : November 16–20, 2020 : EMNLP 2020 (S. 4483-4499). , Association for Computational Linguistics: Stroudsburg, PA.
Lauscher, A., Takieddin, R., Ponzetto, S. P. and Glavaš, G. (2020). AraWEAT: Multidimensional analysis of biases in Arabic word embeddings. In , The Fifth Arabic Natural Language Processing Workshop WANLP 2020, proceedings of the workshop : December 12, 2020, Barcelona, Spain : COLING 2020 (S. 192–199). , Association for Computational Linguistics: Stroudsburg, PA.
Lauscher, A., Vulić, I., Ponti, E. M., Korhonen, A. and Glavaš, G. (2020). Specializing unsupervised pretraining models for word-level semantic similarity. In , The 28th International Conference on Computational Linguistics : proceedings of the conference : December 8–13, 2020 : COLING 2020 (S. 1371-1383). Proceedings of the 28th International Conference on Computational Linguistics, Association for Computational Linguistics, ACL: Straoudsburg. PA.

Yamamoto, S., Lauscher, A., Ponzetto, S. P., Glavaš, G. and Morishima, S. (2021). Visual summary identification from scientific publications via self-supervised learning. Frontiers in Research Metrics and Analytics, 6, 1–13.