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Veröffentlichungsdatum
Anzahl der Publikationen: 7
2022
Hofmann, Valentin; Dong, Xiaowen; Pierrehumbert, Janet und Schütze, Hinrich
(Juli 2022):
Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity.
NAACL 2022, Seattle, United States, July 2022.
Carpuat, Marine; de Marneffe, Marie-Catherine und Meza Ruiz, Ivan Vladimir (Hrsg.):
In: Findings of the Association for Computational Linguistics: NAACL 2022,
Stroudsburg, PA: Association for Computational Linguistics. S. 536-550
[PDF, 3MB]
Hofmann, Valentin; Pierrehumbert, Janet und Schütze, Hinrich
(Juli 2022):
Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology.
39 th International Conference on Machine Learning, Baltimore, Maryland, USA, July 2022.
Chaudhuri, Kamalika; Jegelka, Stefanie; Song, Le; Szepesvari, Csaba; Niu, Gang und Sabato, Sivan (Hrsg.):
In: Proceedings of the 39 th International Conference on Machine Learning, Baltimore, Maryland, USA, PMLR 162, 202,
Bd. 162
S. 8796-8810
[PDF, 32MB]
Hofmann, Valentin; Schütze, Hinrich und Pierrehumbert, Janet
(Juni 2022):
The Reddit Politosphere: A Large-Scale Text and NetworkResource of Online Political Discourse.
ICWSM 2022, Atlanta, Georgia, USA and online, June 6–9, 2022.
Proceedings of the International AAAI Conference on Web and Social Media.
Bd. 16
S. 1259-1267
[PDF, 6MB]
2021
Hofmann, Valentin; Pierrehumbert, Janet und Schütze, Hinrich
(2021):
Superbizarre Is Not Superb: Derivational Morphology Improves BERT’s Interpretation of Complex Words.
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Online, August 1–6, 2021.
In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers),
Association for Computational Linguistics (ACL). S. 3594-3608
2020
Hofmann, Valentin; Pierrehumbert, Janet und Schütze, Hinrich
(November 2020):
DagoBERT: Generating Derivational Morphology with a Pretrained Language Model.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Online, 16. – 20. November 2020.
[PDF, 2MB]
Hofmann, Valentin; Schütze, Hinrich und Pierrehumbert, Janet
(6. Juli 2020):
A Graph Auto-encoder Model of Derivational Morphology.
, July 6 – 8, 2020, Seattle, USA
[PDF, 1MB]
Hofmann, Valentin; Pierrehumbert, Janet und Schütze, Hinrich
(6. Juli 2020):
Predicting the Growth of Morphological Families from Social and Linguistic Factors.
, July 6 – 8, 2020, Seattle, USA
[PDF, 2MB]