Sebastian Wankerl, M.Sc.

Chair of Data Science (Informatik X)
University of Würzburg
Campus Hubland Nord
Emil-Fischer-Straße 50
97074 Würzburg
Germany

Email: sebastian.wankerl[at]uni-wuerzburg.de

Office: Room 50.03.019 (Building G50, Institutsgebäude Künstliche Intelligenz)

Publications

2023[ to top ]

Wankerl, S., Dulny, A., Götz, G., and Hotho, A. (2023) “Can Neural Networks Distinguish High-school Level Mathematical Concepts?”, in Accepted at ICDM.
- [ BibTeX ]
@conference{wankerl2023neural, author = {Wankerl, Sebastian and Dulny, Andrzej and Götz, Gerhard and Hotho, Andreas}, booktitle = {Accepted at ICDM}, keywords = {author:wankerl}, title = {Can Neural Networks Distinguish High-school Level Mathematical Concepts?}, year = 2023 }

2022[ to top ]

Pfister, J., Wankerl, S., and Hotho, A. (2022) “Point me to your Opinion, {S}en{P}oi”, in Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), Seattle, United States: Association for Computational Linguistics, 1313–1323, available: https://aclanthology.org/2022.semeval-1.183.
- [ Abstract ]
- [ BibTeX ]
- [ URL ]
- [ Download ]
Structured Sentiment Analysis is the task of extracting sentiment tuples in a graph structure commonly from review texts. We adapt the Aspect-Based Sentiment Analysis pointer network BARTABSA to model this tuple extraction as a sequence prediction task and extend their output grammar to account for the increased complexity of Structured Sentiment Analysis. To predict structured sentiment tuples in languages other than English we swap BART for a multilingual mT5 and introduce a novel Output Length Regularization to mitigate overfitting to common target sequence lengths, thereby improving the performance of the model by up to 70{\%}. We evaluate our approach on seven datasets in five languages including a zero shot crosslingual setting.

@inproceedings{pfister-etal-2022-senpoi, abstract = {Structured Sentiment Analysis is the task of extracting sentiment tuples in a graph structure commonly from review texts. We adapt the Aspect-Based Sentiment Analysis pointer network BARTABSA to model this tuple extraction as a sequence prediction task and extend their output grammar to account for the increased complexity of Structured Sentiment Analysis. To predict structured sentiment tuples in languages other than English we swap BART for a multilingual mT5 and introduce a novel Output Length Regularization to mitigate overfitting to common target sequence lengths, thereby improving the performance of the model by up to 70{\%}. We evaluate our approach on seven datasets in five languages including a zero shot crosslingual setting.}, address = {Seattle, United States}, author = {Pfister, Jan and Wankerl, Sebastian and Hotho, Andreas}, booktitle = {Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)}, keywords = {from:janpf}, month = {07}, pages = {1313–1323}, publisher = {Association for Computational Linguistics}, title = {Point me to your Opinion, {S}en{P}oi}, year = 2022 }

Hubland Nord

Sebastian Wankerl, M.Sc.

Publications

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