Hu, L., Habernal, I., Shen, L., &; Wang, D. (2024). Differentially Private Natural Language Models: Recent Advances and Future Directions. In Y. Graham &; M. Purver (Eds.), Findings of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024 (pp. 478–499). Association for Computational Linguistics.

Igamberdiev, T., Vu, D. N. L., Kuennecke, F., Yu, Z., Holmer, J., &; Habernal, I. (2024). DP-NMT: Scalable Differentially Private Machine Translation. In N. Aletras &; O. De Clercq (Eds.), Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations (pp. 94–105). Association for Computational Linguistics.

Büttner, M., &; Habernal, I. (2024). Answering legal questions from laymen in German civil law system. In Y. Graham &; M. Purver (Eds.), Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 2015–2027). Association for Computational Linguistics.

Habernal, I., Mireshghallah, F., Thaine, P., Ghanavati, S., &; Feyisetan, O. (2023). Privacy-Preserving Natural Language Processing. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts.https://doi.org/10.18653/v1/2023.eacl-tutorials.6 

Matzken, C., Eger, S., &; Habernal, I. (2023). Trade-Offs Between Fairness and Privacy in Language Modeling. Findings of the Association for Computational Linguistics: ACL 2023.https://doi.org/10.18653/v1/2023.findings-acl.434 

Mouhammad, N., Daxenberger, J., Schiller, B., &; Habernal, I. (2023). Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting. Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII).https://doi.org/10.18653/v1/2023.law-1.8 

Yin, Y., &; Habernal, I. (2023). Privacy-Preserving Models for Legal Natural Language Processing. Proceedings of the Natural Legal Language Processing Workshop 2022.https://doi.org/10.18653/v1/2022.nllp-1.14 

Sawhney, R., Neerkaje, A., Habernal, I., &; Flek, L. (2023). How Much User Context Do We Need? Privacy by Design in Mental Health NLP Applications. Proceedings of the International AAAI Conference on Web and Social Media, 17, 766–776.https://doi.org/10.1609/icwsm.v17i1.22186 

Senge, M., Igamberdiev, T., &; Habernal, I. (2023). One size does not fit all: Investigating strategies for differentially-private learning across NLP tasks. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing.https://doi.org/10.18653/v1/2022.emnlp-main.496 

Igamberdiev, T., &; Habernal, I. (2023). DP-BART for Privatized Text Rewriting under Local Differential Privacy. Findings of the Association for Computational Linguistics: ACL 2023.https://doi.org/10.18653/v1/2023.findings-acl.874 

Bongard, L., Held, L., &; Habernal, I. (2023). The Legal Argument Reasoning Task in Civil Procedure. Proceedings of the Natural Legal Language Processing Workshop 2022.https://doi.org/10.18653/v1/2022.nllp-1.17 

Habernal, I., Faber, D., Recchia, N., Bretthauer, S., Gurevych, I., Spiecker genannt Döhmann, I., &; Burchard, C. (2023). Mining legal arguments in court decisions. Artificial Intelligence and Law.https://doi.org/10.1007/s10506-023-09361-y 

Igamberdiev, T., Vu, D. N. L., Künnecke, F., Yu, Z., Holmer, J., &; Habernal, I. (2023). DP-NMT: Scalable Differentially-Private Machine Translation.

Held, L., &; Habernal, I. (2023). LaCour!: Enabling Research on Argumentation in Hearings of the European Court of Human Rights.

Igamberdiev, T., &; Habernal, I. (2022). Privacy-Preserving Graph Convolutional Networks for Text Classification. Proceedings of the Thirteenth Language Resources and Evaluation Conference, 338–350.

Igamberdiev, T., Arnold, T., &; Habernal, I. (2022). DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting. Proceedings of the 29th International Conference on Computational Linguistics, 2927–2933.

Habernal, I. (2022). How reparametrization trick broke differentially-private text representation learning. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers).https://doi.org/10.18653/v1/2022.acl-short.87 

Habernal, I. (2021). When differential privacy meets NLP: The devil is in the detail. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.https://doi.org/10.18653/v1/2021.emnlp-main.114 

Glockner, M., Habernal, I., &; Gurevych, I. (2020). Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision. Findings of the Association for Computational Linguistics: EMNLP 2020.https://doi.org/10.18653/v1/2020.findings-emnlp.97 

Habernal, I., Wachsmuth, H., Gurevych, I., &; Stein, B. (2018). The Argument Reasoning Comprehension Task: Identification and            Reconstruction of Implicit Warrants. Proceedings of the 2018 Conference of the North American Chapter of          the Association for Computational Linguistics: Human Language          Technologies, Volume 1 (Long Papers).https://doi.org/10.18653/v1/n18-1175