---
_id: '52827'
author:
- first_name: Lijie
full_name: Hu, Lijie
last_name: Hu
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
- first_name: Lei
full_name: Shen, Lei
last_name: Shen
- first_name: Di
full_name: Wang, Di
last_name: Wang
citation:
ama: 'Hu L, Habernal I, Shen L, Wang D. Differentially Private Natural Language
Models: Recent Advances and Future Directions. In: Graham Y, Purver M, eds. Findings
of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta,
March 17-22, 2024. Association for Computational Linguistics; 2024:478–499.'
apa: '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.'
bibtex: '@inproceedings{Hu_Habernal_Shen_Wang_2024, title={Differentially Private
Natural Language Models: Recent Advances and Future Directions}, booktitle={Findings
of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta,
March 17-22, 2024}, publisher={Association for Computational Linguistics}, author={Hu,
Lijie and Habernal, Ivan and Shen, Lei and Wang, Di}, editor={Graham, Yvette and
Purver, Matthew}, year={2024}, pages={478–499} }'
chicago: 'Hu, Lijie, Ivan Habernal, Lei Shen, and Di Wang. “Differentially Private
Natural Language Models: Recent Advances and Future Directions.” In Findings
of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta,
March 17-22, 2024, edited by Yvette Graham and Matthew Purver, 478–499. Association
for Computational Linguistics, 2024.'
ieee: 'L. Hu, I. Habernal, L. Shen, and D. Wang, “Differentially Private Natural
Language Models: Recent Advances and Future Directions,” in Findings of the
Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March
17-22, 2024, 2024, pp. 478–499.'
mla: 'Hu, Lijie, et al. “Differentially Private Natural Language Models: Recent
Advances and Future Directions.” Findings of the Association for Computational
Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024, edited by
Yvette Graham and Matthew Purver, Association for Computational Linguistics, 2024,
pp. 478–499.'
short: 'L. Hu, I. Habernal, L. Shen, D. Wang, in: Y. Graham, M. Purver (Eds.), Findings
of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta,
March 17-22, 2024, Association for Computational Linguistics, 2024, pp. 478–499.'
date_created: 2024-03-25T10:30:32Z
date_updated: 2024-03-25T10:31:30Z
department:
- _id: '820'
editor:
- first_name: Yvette
full_name: Graham, Yvette
last_name: Graham
- first_name: Matthew
full_name: Purver, Matthew
last_name: Purver
language:
- iso: eng
page: 478–499
publication: 'Findings of the Association for Computational Linguistics: EACL 2024,
St. Julian’s, Malta, March 17-22, 2024'
publisher: Association for Computational Linguistics
status: public
title: 'Differentially Private Natural Language Models: Recent Advances and Future
Directions'
type: conference
user_id: '15504'
year: '2024'
...
---
_id: '52842'
abstract:
- lang: eng
text: Neural machine translation (NMT) is a widely popular text generation task,
yet there is a considerable research gap in the development of privacy-preserving
NMT models, despite significant data privacy concerns for NMT systems. Differentially
private stochastic gradient descent (DP-SGD) is a popular method for training
machine learning models with concrete privacy guarantees; however, the implementation
specifics of training a model with DP-SGD are not always clarified in existing
models, with differing software libraries used and code bases not always being
public, leading to reproducibility issues. To tackle this, we introduce DP-NMT,
an open-source framework for carrying out research on privacy-preserving NMT with
DP-SGD, bringing together numerous models, datasets, and evaluation metrics in
one systematic software package. Our goal is to provide a platform for researchers
to advance the development of privacy-preserving NMT systems, keeping the specific
details of the DP-SGD algorithm transparent and intuitive to implement. We run
a set of experiments on datasets from both general and privacy-related domains
to demonstrate our framework in use. We make our framework publicly available
and welcome feedback from the community.
author:
- first_name: Timour
full_name: Igamberdiev, Timour
last_name: Igamberdiev
- first_name: Doan Nam Long
full_name: Vu, Doan Nam Long
last_name: Vu
- first_name: Felix
full_name: Kuennecke, Felix
last_name: Kuennecke
- first_name: Zhuo
full_name: Yu, Zhuo
last_name: Yu
- first_name: Jannik
full_name: Holmer, Jannik
last_name: Holmer
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
citation:
ama: 'Igamberdiev T, Vu DNL, Kuennecke F, Yu Z, Holmer J, Habernal I. DP-NMT: Scalable
Differentially Private Machine Translation. In: Aletras N, De Clercq O, eds. Proceedings
of the 18th Conference of the European Chapter of the Association for Computational
Linguistics: System Demonstrations. Association for Computational Linguistics;
2024:94–105.'
apa: '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.'
bibtex: '@inproceedings{Igamberdiev_Vu_Kuennecke_Yu_Holmer_Habernal_2024, place={St.
Julians, Malta}, title={DP-NMT: Scalable Differentially Private Machine Translation},
booktitle={Proceedings of the 18th Conference of the European Chapter of the Association
for Computational Linguistics: System Demonstrations}, publisher={Association
for Computational Linguistics}, author={Igamberdiev, Timour and Vu, Doan Nam Long
and Kuennecke, Felix and Yu, Zhuo and Holmer, Jannik and Habernal, Ivan}, editor={Aletras,
Nikolaos and De Clercq, Orphee}, year={2024}, pages={94–105} }'
chicago: 'Igamberdiev, Timour, Doan Nam Long Vu, Felix Kuennecke, Zhuo Yu, Jannik
Holmer, and Ivan Habernal. “DP-NMT: Scalable Differentially Private Machine Translation.”
In Proceedings of the 18th Conference of the European Chapter of the Association
for Computational Linguistics: System Demonstrations, edited by Nikolaos Aletras
and Orphee De Clercq, 94–105. St. Julians, Malta: Association for Computational
Linguistics, 2024.'
ieee: 'T. Igamberdiev, D. N. L. Vu, F. Kuennecke, Z. Yu, J. Holmer, and I. Habernal,
“DP-NMT: Scalable Differentially Private Machine Translation,” in Proceedings
of the 18th Conference of the European Chapter of the Association for Computational
Linguistics: System Demonstrations, 2024, pp. 94–105.'
mla: 'Igamberdiev, Timour, et al. “DP-NMT: Scalable Differentially Private Machine
Translation.” Proceedings of the 18th Conference of the European Chapter of
the Association for Computational Linguistics: System Demonstrations, edited
by Nikolaos Aletras and Orphee De Clercq, Association for Computational Linguistics,
2024, pp. 94–105.'
short: 'T. Igamberdiev, D.N.L. Vu, F. Kuennecke, Z. Yu, J. Holmer, I. Habernal,
in: N. Aletras, O. De Clercq (Eds.), Proceedings of the 18th Conference of the
European Chapter of the Association for Computational Linguistics: System Demonstrations,
Association for Computational Linguistics, St. Julians, Malta, 2024, pp. 94–105.'
date_created: 2024-03-25T11:30:44Z
date_updated: 2024-03-25T11:31:12Z
department:
- _id: '820'
editor:
- first_name: Nikolaos
full_name: Aletras, Nikolaos
last_name: Aletras
- first_name: Orphee
full_name: De Clercq, Orphee
last_name: De Clercq
language:
- iso: eng
page: 94–105
place: St. Julians, Malta
publication: 'Proceedings of the 18th Conference of the European Chapter of the Association
for Computational Linguistics: System Demonstrations'
publisher: Association for Computational Linguistics
status: public
title: 'DP-NMT: Scalable Differentially Private Machine Translation'
type: conference
user_id: '15504'
year: '2024'
...
---
_id: '48289'
author:
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
- first_name: Fatemehsadat
full_name: Mireshghallah, Fatemehsadat
last_name: Mireshghallah
- first_name: Patricia
full_name: Thaine, Patricia
last_name: Thaine
- first_name: Sepideh
full_name: Ghanavati, Sepideh
last_name: Ghanavati
- first_name: Oluwaseyi
full_name: Feyisetan, Oluwaseyi
last_name: Feyisetan
citation:
ama: 'Habernal I, Mireshghallah F, Thaine P, Ghanavati S, Feyisetan O. Privacy-Preserving
Natural Language Processing. In: Proceedings of the 17th Conference of the
European Chapter of the Association for Computational Linguistics: Tutorial Abstracts.
Association for Computational Linguistics; 2023. doi:10.18653/v1/2023.eacl-tutorials.6'
apa: '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'
bibtex: '@inproceedings{Habernal_Mireshghallah_Thaine_Ghanavati_Feyisetan_2023,
title={Privacy-Preserving Natural Language Processing}, DOI={10.18653/v1/2023.eacl-tutorials.6},
booktitle={Proceedings of the 17th Conference of the European Chapter of the Association
for Computational Linguistics: Tutorial Abstracts}, publisher={Association for
Computational Linguistics}, author={Habernal, Ivan and Mireshghallah, Fatemehsadat
and Thaine, Patricia and Ghanavati, Sepideh and Feyisetan, Oluwaseyi}, year={2023}
}'
chicago: 'Habernal, Ivan, Fatemehsadat Mireshghallah, Patricia Thaine, Sepideh Ghanavati,
and Oluwaseyi Feyisetan. “Privacy-Preserving Natural Language Processing.” In
Proceedings of the 17th Conference of the European Chapter of the Association
for Computational Linguistics: Tutorial Abstracts. Association for Computational
Linguistics, 2023. https://doi.org/10.18653/v1/2023.eacl-tutorials.6.'
ieee: 'I. Habernal, F. Mireshghallah, P. Thaine, S. Ghanavati, and O. Feyisetan,
“Privacy-Preserving Natural Language Processing,” 2023, doi: 10.18653/v1/2023.eacl-tutorials.6.'
mla: 'Habernal, Ivan, et al. “Privacy-Preserving Natural Language Processing.” Proceedings
of the 17th Conference of the European Chapter of the Association for Computational
Linguistics: Tutorial Abstracts, Association for Computational Linguistics,
2023, doi:10.18653/v1/2023.eacl-tutorials.6.'
short: 'I. Habernal, F. Mireshghallah, P. Thaine, S. Ghanavati, O. Feyisetan, in:
Proceedings of the 17th Conference of the European Chapter of the Association
for Computational Linguistics: Tutorial Abstracts, Association for Computational
Linguistics, 2023.'
date_created: 2023-10-19T08:23:13Z
date_updated: 2023-10-19T12:03:52Z
department:
- _id: '34'
- _id: '820'
doi: 10.18653/v1/2023.eacl-tutorials.6
language:
- iso: eng
publication: 'Proceedings of the 17th Conference of the European Chapter of the Association
for Computational Linguistics: Tutorial Abstracts'
publication_status: published
publisher: Association for Computational Linguistics
status: public
title: Privacy-Preserving Natural Language Processing
type: conference
user_id: '15504'
year: '2023'
...
---
_id: '48288'
author:
- first_name: Cleo
full_name: Matzken, Cleo
last_name: Matzken
- first_name: Steffen
full_name: Eger, Steffen
last_name: Eger
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
citation:
ama: 'Matzken C, Eger S, Habernal I. Trade-Offs Between Fairness and Privacy in
Language Modeling. In: Findings of the Association for Computational Linguistics:
ACL 2023. Association for Computational Linguistics; 2023. doi:10.18653/v1/2023.findings-acl.434'
apa: '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'
bibtex: '@inproceedings{Matzken_Eger_Habernal_2023, title={Trade-Offs Between Fairness
and Privacy in Language Modeling}, DOI={10.18653/v1/2023.findings-acl.434},
booktitle={Findings of the Association for Computational Linguistics: ACL 2023},
publisher={Association for Computational Linguistics}, author={Matzken, Cleo and
Eger, Steffen and Habernal, Ivan}, year={2023} }'
chicago: 'Matzken, Cleo, Steffen Eger, and Ivan Habernal. “Trade-Offs Between Fairness
and Privacy in Language Modeling.” In Findings of the Association for Computational
Linguistics: ACL 2023. Association for Computational Linguistics, 2023. https://doi.org/10.18653/v1/2023.findings-acl.434.'
ieee: 'C. Matzken, S. Eger, and I. Habernal, “Trade-Offs Between Fairness and Privacy
in Language Modeling,” 2023, doi: 10.18653/v1/2023.findings-acl.434.'
mla: 'Matzken, Cleo, et al. “Trade-Offs Between Fairness and Privacy in Language
Modeling.” Findings of the Association for Computational Linguistics: ACL 2023,
Association for Computational Linguistics, 2023, doi:10.18653/v1/2023.findings-acl.434.'
short: 'C. Matzken, S. Eger, I. Habernal, in: Findings of the Association for Computational
Linguistics: ACL 2023, Association for Computational Linguistics, 2023.'
date_created: 2023-10-19T08:22:57Z
date_updated: 2023-10-19T12:04:23Z
department:
- _id: '34'
- _id: '820'
doi: 10.18653/v1/2023.findings-acl.434
language:
- iso: eng
publication: 'Findings of the Association for Computational Linguistics: ACL 2023'
publication_status: published
publisher: Association for Computational Linguistics
status: public
title: Trade-Offs Between Fairness and Privacy in Language Modeling
type: conference
user_id: '15504'
year: '2023'
...
---
_id: '48291'
author:
- first_name: Nina
full_name: Mouhammad, Nina
last_name: Mouhammad
- first_name: Johannes
full_name: Daxenberger, Johannes
last_name: Daxenberger
- first_name: Benjamin
full_name: Schiller, Benjamin
last_name: Schiller
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
citation:
ama: 'Mouhammad N, Daxenberger J, Schiller B, Habernal I. Crowdsourcing on Sensitive
Data with Privacy-Preserving Text Rewriting. In: Proceedings of the 17th Linguistic
Annotation Workshop (LAW-XVII). Association for Computational Linguistics;
2023. doi:10.18653/v1/2023.law-1.8'
apa: 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
bibtex: '@inproceedings{Mouhammad_Daxenberger_Schiller_Habernal_2023, title={Crowdsourcing
on Sensitive Data with Privacy-Preserving Text Rewriting}, DOI={10.18653/v1/2023.law-1.8},
booktitle={Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)},
publisher={Association for Computational Linguistics}, author={Mouhammad, Nina
and Daxenberger, Johannes and Schiller, Benjamin and Habernal, Ivan}, year={2023}
}'
chicago: Mouhammad, Nina, Johannes Daxenberger, Benjamin Schiller, and Ivan Habernal.
“Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting.” In Proceedings
of the 17th Linguistic Annotation Workshop (LAW-XVII). Association for Computational
Linguistics, 2023. https://doi.org/10.18653/v1/2023.law-1.8.
ieee: 'N. Mouhammad, J. Daxenberger, B. Schiller, and I. Habernal, “Crowdsourcing
on Sensitive Data with Privacy-Preserving Text Rewriting,” 2023, doi: 10.18653/v1/2023.law-1.8.'
mla: Mouhammad, Nina, et al. “Crowdsourcing on Sensitive Data with Privacy-Preserving
Text Rewriting.” Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII),
Association for Computational Linguistics, 2023, doi:10.18653/v1/2023.law-1.8.
short: 'N. Mouhammad, J. Daxenberger, B. Schiller, I. Habernal, in: Proceedings
of the 17th Linguistic Annotation Workshop (LAW-XVII), Association for Computational
Linguistics, 2023.'
date_created: 2023-10-19T08:24:06Z
date_updated: 2023-10-19T12:09:35Z
department:
- _id: '34'
- _id: '820'
doi: 10.18653/v1/2023.law-1.8
language:
- iso: eng
publication: Proceedings of the 17th Linguistic Annotation Workshop (LAW-XVII)
publication_status: published
publisher: Association for Computational Linguistics
status: public
title: Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting
type: conference
user_id: '15504'
year: '2023'
...
---
_id: '48296'
author:
- first_name: Ying
full_name: Yin, Ying
last_name: Yin
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
citation:
ama: 'Yin Y, Habernal I. Privacy-Preserving Models for Legal Natural Language Processing.
In: Proceedings of the Natural Legal Language Processing Workshop 2022.
Association for Computational Linguistics; 2023. doi:10.18653/v1/2022.nllp-1.14'
apa: 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
bibtex: '@inproceedings{Yin_Habernal_2023, title={Privacy-Preserving Models for
Legal Natural Language Processing}, DOI={10.18653/v1/2022.nllp-1.14},
booktitle={Proceedings of the Natural Legal Language Processing Workshop 2022},
publisher={Association for Computational Linguistics}, author={Yin, Ying and Habernal,
Ivan}, year={2023} }'
chicago: Yin, Ying, and Ivan Habernal. “Privacy-Preserving Models for Legal Natural
Language Processing.” In Proceedings of the Natural Legal Language Processing
Workshop 2022. Association for Computational Linguistics, 2023. https://doi.org/10.18653/v1/2022.nllp-1.14.
ieee: 'Y. Yin and I. Habernal, “Privacy-Preserving Models for Legal Natural Language
Processing,” 2023, doi: 10.18653/v1/2022.nllp-1.14.'
mla: Yin, Ying, and Ivan Habernal. “Privacy-Preserving Models for Legal Natural
Language Processing.” Proceedings of the Natural Legal Language Processing
Workshop 2022, Association for Computational Linguistics, 2023, doi:10.18653/v1/2022.nllp-1.14.
short: 'Y. Yin, I. Habernal, in: Proceedings of the Natural Legal Language Processing
Workshop 2022, Association for Computational Linguistics, 2023.'
date_created: 2023-10-19T08:26:13Z
date_updated: 2023-10-19T12:09:24Z
department:
- _id: '34'
- _id: '820'
doi: 10.18653/v1/2022.nllp-1.14
language:
- iso: eng
publication: Proceedings of the Natural Legal Language Processing Workshop 2022
publication_status: published
publisher: Association for Computational Linguistics
status: public
title: Privacy-Preserving Models for Legal Natural Language Processing
type: conference
user_id: '15504'
year: '2023'
...
---
_id: '48294'
abstract:
- lang: eng
text: Clinical NLP tasks such as mental health assessment from text, must
take social constraints into account - the performance maximization must be constrained
by the utmost importance of guaranteeing privacy of user data. Consumer protection
regulations, such as GDPR, generally handle privacy by restricting data availability,
such as requiring to limit user data to 'what is necessary' for a given purpose.
In this work, we reason that providing stricter formal privacy guarantees, while
increasing the volume of user data in the model, in most cases increases benefit
for all parties involved, especially for the user. We demonstrate our arguments
on two existing suicide risk assessment datasets of Twitter and Reddit posts.
We present the first analysis juxtaposing user history length and differential
privacy budgets and elaborate how modeling additional user context enables utility
preservation while maintaining acceptable user privacy guarantees.
author:
- first_name: Ramit
full_name: Sawhney, Ramit
last_name: Sawhney
- first_name: Atula
full_name: Neerkaje, Atula
last_name: Neerkaje
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
- first_name: Lucie
full_name: Flek, Lucie
last_name: Flek
citation:
ama: Sawhney R, Neerkaje A, Habernal I, Flek L. 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. 2023;17:766-776. doi:10.1609/icwsm.v17i1.22186
apa: 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
bibtex: '@article{Sawhney_Neerkaje_Habernal_Flek_2023, title={How Much User Context
Do We Need? Privacy by Design in Mental Health NLP Applications}, volume={17},
DOI={10.1609/icwsm.v17i1.22186},
journal={Proceedings of the International AAAI Conference on Web and Social Media},
publisher={Association for the Advancement of Artificial Intelligence (AAAI)},
author={Sawhney, Ramit and Neerkaje, Atula and Habernal, Ivan and Flek, Lucie},
year={2023}, pages={766–776} }'
chicago: 'Sawhney, Ramit, Atula Neerkaje, Ivan Habernal, and Lucie Flek. “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 (2023): 766–76. https://doi.org/10.1609/icwsm.v17i1.22186.'
ieee: 'R. Sawhney, A. Neerkaje, I. Habernal, and L. Flek, “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, vol. 17, pp.
766–776, 2023, doi: 10.1609/icwsm.v17i1.22186.'
mla: Sawhney, Ramit, et al. “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, vol. 17, Association for the Advancement of Artificial
Intelligence (AAAI), 2023, pp. 766–76, doi:10.1609/icwsm.v17i1.22186.
short: R. Sawhney, A. Neerkaje, I. Habernal, L. Flek, Proceedings of the International
AAAI Conference on Web and Social Media 17 (2023) 766–776.
date_created: 2023-10-19T08:25:46Z
date_updated: 2023-10-19T12:06:29Z
department:
- _id: '34'
- _id: '820'
doi: 10.1609/icwsm.v17i1.22186
intvolume: ' 17'
language:
- iso: eng
page: 766-776
publication: Proceedings of the International AAAI Conference on Web and Social Media
publication_identifier:
issn:
- 2334-0770
- 2162-3449
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence (AAAI)
status: public
title: How Much User Context Do We Need? Privacy by Design in Mental Health NLP Applications
type: journal_article
user_id: '15504'
volume: 17
year: '2023'
...
---
_id: '48297'
author:
- first_name: Manuel
full_name: Senge, Manuel
last_name: Senge
- first_name: Timour
full_name: Igamberdiev, Timour
last_name: Igamberdiev
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
citation:
ama: 'Senge M, Igamberdiev T, Habernal I. One size does not fit all: Investigating
strategies for differentially-private learning across NLP tasks. In: Proceedings
of the 2022 Conference on Empirical Methods in Natural Language Processing.
Association for Computational Linguistics; 2023. doi:10.18653/v1/2022.emnlp-main.496'
apa: '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'
bibtex: '@inproceedings{Senge_Igamberdiev_Habernal_2023, title={One size does not
fit all: Investigating strategies for differentially-private learning across NLP
tasks}, DOI={10.18653/v1/2022.emnlp-main.496},
booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural
Language Processing}, publisher={Association for Computational Linguistics}, author={Senge,
Manuel and Igamberdiev, Timour and Habernal, Ivan}, year={2023} }'
chicago: 'Senge, Manuel, Timour Igamberdiev, and Ivan Habernal. “One Size Does Not
Fit All: Investigating Strategies for Differentially-Private Learning across NLP
Tasks.” In Proceedings of the 2022 Conference on Empirical Methods in Natural
Language Processing. Association for Computational Linguistics, 2023. https://doi.org/10.18653/v1/2022.emnlp-main.496.'
ieee: 'M. Senge, T. Igamberdiev, and I. Habernal, “One size does not fit all: Investigating
strategies for differentially-private learning across NLP tasks,” 2023, doi: 10.18653/v1/2022.emnlp-main.496.'
mla: 'Senge, Manuel, et al. “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, Association
for Computational Linguistics, 2023, doi:10.18653/v1/2022.emnlp-main.496.'
short: 'M. Senge, T. Igamberdiev, I. Habernal, in: Proceedings of the 2022 Conference
on Empirical Methods in Natural Language Processing, Association for Computational
Linguistics, 2023.'
date_created: 2023-10-19T08:26:21Z
date_updated: 2023-10-19T12:05:55Z
department:
- _id: '34'
- _id: '820'
doi: 10.18653/v1/2022.emnlp-main.496
language:
- iso: eng
publication: Proceedings of the 2022 Conference on Empirical Methods in Natural Language
Processing
publication_status: published
publisher: Association for Computational Linguistics
status: public
title: 'One size does not fit all: Investigating strategies for differentially-private
learning across NLP tasks'
type: conference
user_id: '15504'
year: '2023'
...
---
_id: '48292'
author:
- first_name: Timour
full_name: Igamberdiev, Timour
last_name: Igamberdiev
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
citation:
ama: 'Igamberdiev T, Habernal I. DP-BART for Privatized Text Rewriting under Local
Differential Privacy. In: Findings of the Association for Computational Linguistics:
ACL 2023. Association for Computational Linguistics; 2023. doi:10.18653/v1/2023.findings-acl.874'
apa: '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'
bibtex: '@inproceedings{Igamberdiev_Habernal_2023, title={DP-BART for Privatized
Text Rewriting under Local Differential Privacy}, DOI={10.18653/v1/2023.findings-acl.874},
booktitle={Findings of the Association for Computational Linguistics: ACL 2023},
publisher={Association for Computational Linguistics}, author={Igamberdiev, Timour
and Habernal, Ivan}, year={2023} }'
chicago: 'Igamberdiev, Timour, and Ivan Habernal. “DP-BART for Privatized Text Rewriting
under Local Differential Privacy.” In Findings of the Association for Computational
Linguistics: ACL 2023. Association for Computational Linguistics, 2023. https://doi.org/10.18653/v1/2023.findings-acl.874.'
ieee: 'T. Igamberdiev and I. Habernal, “DP-BART for Privatized Text Rewriting under
Local Differential Privacy,” 2023, doi: 10.18653/v1/2023.findings-acl.874.'
mla: 'Igamberdiev, Timour, and Ivan Habernal. “DP-BART for Privatized Text Rewriting
under Local Differential Privacy.” Findings of the Association for Computational
Linguistics: ACL 2023, Association for Computational Linguistics, 2023, doi:10.18653/v1/2023.findings-acl.874.'
short: 'T. Igamberdiev, I. Habernal, in: Findings of the Association for Computational
Linguistics: ACL 2023, Association for Computational Linguistics, 2023.'
date_created: 2023-10-19T08:25:19Z
date_updated: 2023-10-19T12:06:47Z
department:
- _id: '34'
- _id: '820'
doi: 10.18653/v1/2023.findings-acl.874
language:
- iso: eng
publication: 'Findings of the Association for Computational Linguistics: ACL 2023'
publication_status: published
publisher: Association for Computational Linguistics
status: public
title: DP-BART for Privatized Text Rewriting under Local Differential Privacy
type: conference
user_id: '15504'
year: '2023'
...
---
_id: '48295'
author:
- first_name: Leonard
full_name: Bongard, Leonard
last_name: Bongard
- first_name: Lena
full_name: Held, Lena
last_name: Held
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
citation:
ama: 'Bongard L, Held L, Habernal I. The Legal Argument Reasoning Task in Civil
Procedure. In: Proceedings of the Natural Legal Language Processing Workshop
2022. Association for Computational Linguistics; 2023. doi:10.18653/v1/2022.nllp-1.17'
apa: 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
bibtex: '@inproceedings{Bongard_Held_Habernal_2023, title={The Legal Argument Reasoning
Task in Civil Procedure}, DOI={10.18653/v1/2022.nllp-1.17},
booktitle={Proceedings of the Natural Legal Language Processing Workshop 2022},
publisher={Association for Computational Linguistics}, author={Bongard, Leonard
and Held, Lena and Habernal, Ivan}, year={2023} }'
chicago: Bongard, Leonard, Lena Held, and Ivan Habernal. “The Legal Argument Reasoning
Task in Civil Procedure.” In Proceedings of the Natural Legal Language Processing
Workshop 2022. Association for Computational Linguistics, 2023. https://doi.org/10.18653/v1/2022.nllp-1.17.
ieee: 'L. Bongard, L. Held, and I. Habernal, “The Legal Argument Reasoning Task
in Civil Procedure,” 2023, doi: 10.18653/v1/2022.nllp-1.17.'
mla: Bongard, Leonard, et al. “The Legal Argument Reasoning Task in Civil Procedure.”
Proceedings of the Natural Legal Language Processing Workshop 2022, Association
for Computational Linguistics, 2023, doi:10.18653/v1/2022.nllp-1.17.
short: 'L. Bongard, L. Held, I. Habernal, in: Proceedings of the Natural Legal Language
Processing Workshop 2022, Association for Computational Linguistics, 2023.'
date_created: 2023-10-19T08:26:03Z
date_updated: 2023-10-19T12:06:12Z
department:
- _id: '34'
- _id: '820'
doi: 10.18653/v1/2022.nllp-1.17
language:
- iso: eng
publication: Proceedings of the Natural Legal Language Processing Workshop 2022
publication_status: published
publisher: Association for Computational Linguistics
status: public
title: The Legal Argument Reasoning Task in Civil Procedure
type: conference
user_id: '15504'
year: '2023'
...
---
_id: '48290'
abstract:
- lang: eng
text: AbstractIdentifying, classifying, and analyzing
arguments in legal discourse has been a prominent area of research since the inception
of the argument mining field. However, there has been a major discrepancy between
the way natural language processing (NLP) researchers model and annotate arguments
in court decisions and the way legal experts understand and analyze legal argumentation.
While computational approaches typically simplify arguments into generic premises
and claims, arguments in legal research usually exhibit a rich typology that is
important for gaining insights into the particular case and applications of law
in general. We address this problem and make several substantial contributions
to move the field forward. First, we design a new annotation scheme for legal
arguments in proceedings of the European Court of Human Rights (ECHR) that is
deeply rooted in the theory and practice of legal argumentation research. Second,
we compile and annotate a large corpus of 373 court decisions (2.3M tokens and
15k annotated argument spans). Finally, we train an argument mining model that
outperforms state-of-the-art models in the legal NLP domain and provide a thorough
expert-based evaluation. All datasets and source codes are available under open
lincenses at https://github.com/trusthlt/mining-legal-arguments.
author:
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
- first_name: Daniel
full_name: Faber, Daniel
last_name: Faber
- first_name: Nicola
full_name: Recchia, Nicola
last_name: Recchia
- first_name: Sebastian
full_name: Bretthauer, Sebastian
last_name: Bretthauer
- first_name: Iryna
full_name: Gurevych, Iryna
last_name: Gurevych
- first_name: Indra
full_name: Spiecker genannt Döhmann, Indra
last_name: Spiecker genannt Döhmann
- first_name: Christoph
full_name: Burchard, Christoph
last_name: Burchard
citation:
ama: Habernal I, Faber D, Recchia N, et al. Mining legal arguments in court decisions.
Artificial Intelligence and Law. Published online 2023. doi:10.1007/s10506-023-09361-y
apa: 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
bibtex: '@article{Habernal_Faber_Recchia_Bretthauer_Gurevych_Spiecker genannt Döhmann_Burchard_2023,
title={Mining legal arguments in court decisions}, DOI={10.1007/s10506-023-09361-y},
journal={Artificial Intelligence and Law}, publisher={Springer Science and Business
Media LLC}, author={Habernal, Ivan and Faber, Daniel and Recchia, Nicola and Bretthauer,
Sebastian and Gurevych, Iryna and Spiecker genannt Döhmann, Indra and Burchard,
Christoph}, year={2023} }'
chicago: Habernal, Ivan, Daniel Faber, Nicola Recchia, Sebastian Bretthauer, Iryna
Gurevych, Indra Spiecker genannt Döhmann, and Christoph Burchard. “Mining Legal
Arguments in Court Decisions.” Artificial Intelligence and Law, 2023. https://doi.org/10.1007/s10506-023-09361-y.
ieee: 'I. Habernal et al., “Mining legal arguments in court decisions,” Artificial
Intelligence and Law, 2023, doi: 10.1007/s10506-023-09361-y.'
mla: Habernal, Ivan, et al. “Mining Legal Arguments in Court Decisions.” Artificial
Intelligence and Law, Springer Science and Business Media LLC, 2023, doi:10.1007/s10506-023-09361-y.
short: I. Habernal, D. Faber, N. Recchia, S. Bretthauer, I. Gurevych, I. Spiecker
genannt Döhmann, C. Burchard, Artificial Intelligence and Law (2023).
date_created: 2023-10-19T08:23:39Z
date_updated: 2023-10-19T12:10:02Z
department:
- _id: '34'
- _id: '820'
doi: 10.1007/s10506-023-09361-y
keyword:
- Law
- Artificial Intelligence
language:
- iso: eng
publication: Artificial Intelligence and Law
publication_identifier:
issn:
- 0924-8463
- 1572-8382
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: Mining legal arguments in court decisions
type: journal_article
user_id: '15504'
year: '2023'
...
---
_id: '49649'
author:
- first_name: Timour
full_name: Igamberdiev, Timour
last_name: Igamberdiev
- first_name: Doan Nam Long
full_name: Vu, Doan Nam Long
last_name: Vu
- first_name: Felix
full_name: Künnecke, Felix
last_name: Künnecke
- first_name: Zhuo
full_name: Yu, Zhuo
last_name: Yu
- first_name: Jannik
full_name: Holmer, Jannik
last_name: Holmer
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
citation:
ama: 'Igamberdiev T, Vu DNL, Künnecke F, Yu Z, Holmer J, Habernal I. DP-NMT: Scalable
Differentially-Private Machine Translation. Published online 2023.'
apa: 'Igamberdiev, T., Vu, D. N. L., Künnecke, F., Yu, Z., Holmer, J., & Habernal,
I. (2023). DP-NMT: Scalable Differentially-Private Machine Translation.'
bibtex: '@article{Igamberdiev_Vu_Künnecke_Yu_Holmer_Habernal_2023, title={DP-NMT:
Scalable Differentially-Private Machine Translation}, author={Igamberdiev, Timour
and Vu, Doan Nam Long and Künnecke, Felix and Yu, Zhuo and Holmer, Jannik and
Habernal, Ivan}, year={2023} }'
chicago: 'Igamberdiev, Timour, Doan Nam Long Vu, Felix Künnecke, Zhuo Yu, Jannik
Holmer, and Ivan Habernal. “DP-NMT: Scalable Differentially-Private Machine Translation,”
2023.'
ieee: 'T. Igamberdiev, D. N. L. Vu, F. Künnecke, Z. Yu, J. Holmer, and I. Habernal,
“DP-NMT: Scalable Differentially-Private Machine Translation.” 2023.'
mla: 'Igamberdiev, Timour, et al. DP-NMT: Scalable Differentially-Private Machine
Translation. 2023.'
short: T. Igamberdiev, D.N.L. Vu, F. Künnecke, Z. Yu, J. Holmer, I. Habernal, (2023).
date_created: 2023-12-15T07:00:42Z
date_updated: 2023-12-15T07:21:45Z
department:
- _id: '820'
- _id: '34'
language:
- iso: eng
status: public
title: 'DP-NMT: Scalable Differentially-Private Machine Translation'
type: preprint
user_id: '15504'
year: '2023'
...
---
_id: '49650'
author:
- first_name: Lena
full_name: Held, Lena
last_name: Held
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
citation:
ama: 'Held L, Habernal I. LaCour!: Enabling Research on Argumentation in Hearings
of the European Court of Human Rights. Published online 2023.'
apa: 'Held, L., & Habernal, I. (2023). LaCour!: Enabling Research on Argumentation
in Hearings of the European Court of Human Rights.'
bibtex: '@article{Held_Habernal_2023, title={LaCour!: Enabling Research on Argumentation
in Hearings of the European Court of Human Rights}, author={Held, Lena and Habernal,
Ivan}, year={2023} }'
chicago: 'Held, Lena, and Ivan Habernal. “LaCour!: Enabling Research on Argumentation
in Hearings of the European Court of Human Rights,” 2023.'
ieee: 'L. Held and I. Habernal, “LaCour!: Enabling Research on Argumentation in
Hearings of the European Court of Human Rights.” 2023.'
mla: 'Held, Lena, and Ivan Habernal. LaCour!: Enabling Research on Argumentation
in Hearings of the European Court of Human Rights. 2023.'
short: L. Held, I. Habernal, (2023).
date_created: 2023-12-15T07:18:35Z
date_updated: 2023-12-15T07:21:39Z
department:
- _id: '820'
- _id: '34'
language:
- iso: eng
status: public
title: 'LaCour!: Enabling Research on Argumentation in Hearings of the European Court
of Human Rights'
type: preprint
user_id: '15504'
year: '2023'
...
---
_id: '48299'
abstract:
- lang: eng
text: Graph convolutional networks (GCNs) are a powerful architecture for representation
learning on documents that naturally occur as graphs, e.g., citation or social
networks. However, sensitive personal information, such as documents with people{’}s
profiles or relationships as edges, are prone to privacy leaks, as the trained
model might reveal the original input. Although differential privacy (DP) offers
a well-founded privacy-preserving framework, GCNs pose theoretical and practical
challenges due to their training specifics. We address these challenges by adapting
differentially-private gradient-based training to GCNs and conduct experiments
using two optimizers on five NLP datasets in two languages. We propose a simple
yet efficient method based on random graph splits that not only improves the baseline
privacy bounds by a factor of 2.7 while retaining competitive F1 scores, but also
provides strong privacy guarantees of epsilon = 1.0. We show that, under certain
modeling choices, privacy-preserving GCNs perform up to 90{%} of their non-private
variants, while formally guaranteeing strong privacy measures.
author:
- first_name: Timour
full_name: Igamberdiev, Timour
last_name: Igamberdiev
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
citation:
ama: 'Igamberdiev T, Habernal I. Privacy-Preserving Graph Convolutional Networks
for Text Classification. In: Proceedings of the Thirteenth Language Resources
and Evaluation Conference. European Language Resources Association; 2022:338–350.'
apa: Igamberdiev, T., & Habernal, I. (2022). Privacy-Preserving Graph Convolutional
Networks for Text Classification. Proceedings of the Thirteenth Language Resources
and Evaluation Conference, 338–350.
bibtex: '@inproceedings{Igamberdiev_Habernal_2022, place={Marseille, France}, title={Privacy-Preserving
Graph Convolutional Networks for Text Classification}, booktitle={Proceedings
of the Thirteenth Language Resources and Evaluation Conference}, publisher={European
Language Resources Association}, author={Igamberdiev, Timour and Habernal, Ivan},
year={2022}, pages={338–350} }'
chicago: 'Igamberdiev, Timour, and Ivan Habernal. “Privacy-Preserving Graph Convolutional
Networks for Text Classification.” In Proceedings of the Thirteenth Language
Resources and Evaluation Conference, 338–350. Marseille, France: European
Language Resources Association, 2022.'
ieee: T. Igamberdiev and I. Habernal, “Privacy-Preserving Graph Convolutional Networks
for Text Classification,” in Proceedings of the Thirteenth Language Resources
and Evaluation Conference, 2022, pp. 338–350.
mla: Igamberdiev, Timour, and Ivan Habernal. “Privacy-Preserving Graph Convolutional
Networks for Text Classification.” Proceedings of the Thirteenth Language Resources
and Evaluation Conference, European Language Resources Association, 2022,
pp. 338–350.
short: 'T. Igamberdiev, I. Habernal, in: Proceedings of the Thirteenth Language
Resources and Evaluation Conference, European Language Resources Association,
Marseille, France, 2022, pp. 338–350.'
date_created: 2023-10-19T08:26:58Z
date_updated: 2023-10-19T12:05:12Z
department:
- _id: '34'
- _id: '820'
language:
- iso: eng
page: 338–350
place: Marseille, France
publication: Proceedings of the Thirteenth Language Resources and Evaluation Conference
publisher: European Language Resources Association
status: public
title: Privacy-Preserving Graph Convolutional Networks for Text Classification
type: conference
user_id: '15504'
year: '2022'
...
---
_id: '48300'
abstract:
- lang: eng
text: Text rewriting with differential privacy (DP) provides concrete theoretical
guarantees for protecting the privacy of individuals in textual documents. In
practice, existing systems may lack the means to validate their privacy-preserving
claims, leading to problems of transparency and reproducibility. We introduce
DP-Rewrite, an open-source framework for differentially private text rewriting
which aims to solve these problems by being modular, extensible, and highly customizable.
Our system incorporates a variety of downstream datasets, models, pre-training
procedures, and evaluation metrics to provide a flexible way to lead and validate
private text rewriting research. To demonstrate our software in practice, we provide
a set of experiments as a case study on the ADePT DP text rewriting system, detecting
a privacy leak in its pre-training approach. Our system is publicly available,
and we hope that it will help the community to make DP text rewriting research
more accessible and transparent.
author:
- first_name: Timour
full_name: Igamberdiev, Timour
last_name: Igamberdiev
- first_name: Thomas
full_name: Arnold, Thomas
last_name: Arnold
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
citation:
ama: 'Igamberdiev T, Arnold T, Habernal I. DP-Rewrite: Towards Reproducibility and
Transparency in Differentially Private Text Rewriting. In: Proceedings of the
29th International Conference on Computational Linguistics. International
Committee on Computational Linguistics; 2022:2927–2933.'
apa: '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.'
bibtex: '@inproceedings{Igamberdiev_Arnold_Habernal_2022, place={Gyeongju, Republic
of Korea}, title={DP-Rewrite: Towards Reproducibility and Transparency in Differentially
Private Text Rewriting}, booktitle={Proceedings of the 29th International Conference
on Computational Linguistics}, publisher={International Committee on Computational
Linguistics}, author={Igamberdiev, Timour and Arnold, Thomas and Habernal, Ivan},
year={2022}, pages={2927–2933} }'
chicago: 'Igamberdiev, Timour, Thomas Arnold, and Ivan Habernal. “DP-Rewrite: Towards
Reproducibility and Transparency in Differentially Private Text Rewriting.” In
Proceedings of the 29th International Conference on Computational Linguistics,
2927–2933. Gyeongju, Republic of Korea: International Committee on Computational
Linguistics, 2022.'
ieee: 'T. Igamberdiev, T. Arnold, and I. Habernal, “DP-Rewrite: Towards Reproducibility
and Transparency in Differentially Private Text Rewriting,” in Proceedings
of the 29th International Conference on Computational Linguistics, 2022, pp.
2927–2933.'
mla: 'Igamberdiev, Timour, et al. “DP-Rewrite: Towards Reproducibility and Transparency
in Differentially Private Text Rewriting.” Proceedings of the 29th International
Conference on Computational Linguistics, International Committee on Computational
Linguistics, 2022, pp. 2927–2933.'
short: 'T. Igamberdiev, T. Arnold, I. Habernal, in: Proceedings of the 29th International
Conference on Computational Linguistics, International Committee on Computational
Linguistics, Gyeongju, Republic of Korea, 2022, pp. 2927–2933.'
date_created: 2023-10-19T08:27:05Z
date_updated: 2023-10-19T12:04:57Z
department:
- _id: '34'
- _id: '820'
language:
- iso: eng
page: 2927–2933
place: Gyeongju, Republic of Korea
publication: Proceedings of the 29th International Conference on Computational Linguistics
publisher: International Committee on Computational Linguistics
status: public
title: 'DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private
Text Rewriting'
type: conference
user_id: '15504'
year: '2022'
...
---
_id: '48298'
author:
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
citation:
ama: 'Habernal I. How reparametrization trick broke differentially-private text
representation learning. In: Proceedings of the 60th Annual Meeting of the
Association for Computational Linguistics (Volume 2: Short Papers). Association
for Computational Linguistics; 2022. doi:10.18653/v1/2022.acl-short.87'
apa: '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'
bibtex: '@inproceedings{Habernal_2022, title={How reparametrization trick broke
differentially-private text representation learning}, DOI={10.18653/v1/2022.acl-short.87},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational
Linguistics (Volume 2: Short Papers)}, publisher={Association for Computational
Linguistics}, author={Habernal, Ivan}, year={2022} }'
chicago: 'Habernal, Ivan. “How Reparametrization Trick Broke Differentially-Private
Text Representation Learning.” In Proceedings of the 60th Annual Meeting of
the Association for Computational Linguistics (Volume 2: Short Papers). Association
for Computational Linguistics, 2022. https://doi.org/10.18653/v1/2022.acl-short.87.'
ieee: 'I. Habernal, “How reparametrization trick broke differentially-private text
representation learning,” 2022, doi: 10.18653/v1/2022.acl-short.87.'
mla: 'Habernal, Ivan. “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), Association
for Computational Linguistics, 2022, doi:10.18653/v1/2022.acl-short.87.'
short: 'I. Habernal, in: Proceedings of the 60th Annual Meeting of the Association
for Computational Linguistics (Volume 2: Short Papers), Association for Computational
Linguistics, 2022.'
date_created: 2023-10-19T08:26:35Z
date_updated: 2023-10-19T12:05:39Z
department:
- _id: '34'
- _id: '820'
doi: 10.18653/v1/2022.acl-short.87
language:
- iso: eng
publication: 'Proceedings of the 60th Annual Meeting of the Association for Computational
Linguistics (Volume 2: Short Papers)'
publication_status: published
publisher: Association for Computational Linguistics
status: public
title: How reparametrization trick broke differentially-private text representation
learning
type: conference
user_id: '15504'
year: '2022'
...
---
_id: '48286'
author:
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
citation:
ama: 'Habernal I. When differential privacy meets NLP: The devil is in the detail.
In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language
Processing. Association for Computational Linguistics; 2021. doi:10.18653/v1/2021.emnlp-main.114'
apa: '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'
bibtex: '@inproceedings{Habernal_2021, title={When differential privacy meets NLP:
The devil is in the detail}, DOI={10.18653/v1/2021.emnlp-main.114},
booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural
Language Processing}, publisher={Association for Computational Linguistics}, author={Habernal,
Ivan}, year={2021} }'
chicago: 'Habernal, Ivan. “When Differential Privacy Meets NLP: The Devil Is in
the Detail.” In Proceedings of the 2021 Conference on Empirical Methods in
Natural Language Processing. Association for Computational Linguistics, 2021.
https://doi.org/10.18653/v1/2021.emnlp-main.114.'
ieee: 'I. Habernal, “When differential privacy meets NLP: The devil is in the detail,”
2021, doi: 10.18653/v1/2021.emnlp-main.114.'
mla: 'Habernal, Ivan. “When Differential Privacy Meets NLP: The Devil Is in the
Detail.” Proceedings of the 2021 Conference on Empirical Methods in Natural
Language Processing, Association for Computational Linguistics, 2021, doi:10.18653/v1/2021.emnlp-main.114.'
short: 'I. Habernal, in: Proceedings of the 2021 Conference on Empirical Methods
in Natural Language Processing, Association for Computational Linguistics, 2021.'
date_created: 2023-10-19T08:21:43Z
date_updated: 2023-10-19T12:04:40Z
department:
- _id: '34'
- _id: '820'
doi: 10.18653/v1/2021.emnlp-main.114
language:
- iso: eng
publication: Proceedings of the 2021 Conference on Empirical Methods in Natural Language
Processing
publication_status: published
publisher: Association for Computational Linguistics
status: public
title: 'When differential privacy meets NLP: The devil is in the detail'
type: conference
user_id: '15504'
year: '2021'
...
---
_id: '48301'
author:
- first_name: Max
full_name: Glockner, Max
last_name: Glockner
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
- first_name: Iryna
full_name: Gurevych, Iryna
last_name: Gurevych
citation:
ama: 'Glockner M, Habernal I, Gurevych I. Why do you think that? Exploring Faithful
Sentence-Level Rationales Without Supervision. In: Findings of the Association
for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics;
2020. doi:10.18653/v1/2020.findings-emnlp.97'
apa: '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'
bibtex: '@inproceedings{Glockner_Habernal_Gurevych_2020, title={Why do you think
that? Exploring Faithful Sentence-Level Rationales Without Supervision}, DOI={10.18653/v1/2020.findings-emnlp.97},
booktitle={Findings of the Association for Computational Linguistics: EMNLP 2020},
publisher={Association for Computational Linguistics}, author={Glockner, Max and
Habernal, Ivan and Gurevych, Iryna}, year={2020} }'
chicago: 'Glockner, Max, Ivan Habernal, and Iryna Gurevych. “Why Do You Think That?
Exploring Faithful Sentence-Level Rationales Without Supervision.” In Findings
of the Association for Computational Linguistics: EMNLP 2020. Association
for Computational Linguistics, 2020. https://doi.org/10.18653/v1/2020.findings-emnlp.97.'
ieee: 'M. Glockner, I. Habernal, and I. Gurevych, “Why do you think that? Exploring
Faithful Sentence-Level Rationales Without Supervision,” 2020, doi: 10.18653/v1/2020.findings-emnlp.97.'
mla: 'Glockner, Max, et al. “Why Do You Think That? Exploring Faithful Sentence-Level
Rationales Without Supervision.” Findings of the Association for Computational
Linguistics: EMNLP 2020, Association for Computational Linguistics, 2020,
doi:10.18653/v1/2020.findings-emnlp.97.'
short: 'M. Glockner, I. Habernal, I. Gurevych, in: Findings of the Association for
Computational Linguistics: EMNLP 2020, Association for Computational Linguistics,
2020.'
date_created: 2023-10-19T08:29:15Z
date_updated: 2023-10-19T12:10:18Z
department:
- _id: '34'
- _id: '820'
doi: 10.18653/v1/2020.findings-emnlp.97
language:
- iso: eng
publication: 'Findings of the Association for Computational Linguistics: EMNLP 2020'
publication_status: published
publisher: Association for Computational Linguistics
status: public
title: Why do you think that? Exploring Faithful Sentence-Level Rationales Without
Supervision
type: conference
user_id: '15504'
year: '2020'
...
---
_id: '48303'
author:
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
- first_name: Henning
full_name: Wachsmuth, Henning
last_name: Wachsmuth
- first_name: Iryna
full_name: Gurevych, Iryna
last_name: Gurevych
- first_name: Benno
full_name: Stein, Benno
last_name: Stein
citation:
ama: 'Habernal I, Wachsmuth H, Gurevych I, Stein B. The Argument Reasoning Comprehension
Task: Identification and Reconstruction of Implicit Warrants. In: Proceedings
of the 2018 Conference of the North American Chapter of the Association
for Computational Linguistics: Human Language Technologies, Volume 1
(Long Papers). Association for Computational Linguistics; 2018. doi:10.18653/v1/n18-1175'
apa: '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'
bibtex: '@inproceedings{Habernal_Wachsmuth_Gurevych_Stein_2018, title={The Argument
Reasoning Comprehension Task: Identification and Reconstruction of
Implicit Warrants}, DOI={10.18653/v1/n18-1175},
booktitle={Proceedings of the 2018 Conference of the North American Chapter of
the Association for Computational Linguistics: Human Language
Technologies, Volume 1 (Long Papers)}, publisher={Association for Computational
Linguistics}, author={Habernal, Ivan and Wachsmuth, Henning and Gurevych, Iryna
and Stein, Benno}, year={2018} }'
chicago: 'Habernal, Ivan, Henning Wachsmuth, Iryna Gurevych, and Benno Stein. “The
Argument Reasoning Comprehension Task: Identification and Reconstruction
of Implicit Warrants.” In Proceedings of the 2018 Conference of the North American
Chapter of the Association for Computational Linguistics: Human Language
Technologies, Volume 1 (Long Papers). Association for Computational
Linguistics, 2018. https://doi.org/10.18653/v1/n18-1175.'
ieee: 'I. Habernal, H. Wachsmuth, I. Gurevych, and B. Stein, “The Argument Reasoning
Comprehension Task: Identification and Reconstruction of Implicit Warrants,”
2018, doi: 10.18653/v1/n18-1175.'
mla: 'Habernal, Ivan, et al. “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),
Association for Computational Linguistics, 2018, doi:10.18653/v1/n18-1175.'
short: 'I. Habernal, H. Wachsmuth, I. Gurevych, B. Stein, in: Proceedings of the
2018 Conference of the North American Chapter of the Association for
Computational Linguistics: Human Language Technologies, Volume 1 (Long
Papers), Association for Computational Linguistics, 2018.'
date_created: 2023-10-19T08:29:43Z
date_updated: 2023-10-19T12:09:02Z
department:
- _id: '34'
- _id: '820'
doi: 10.18653/v1/n18-1175
language:
- iso: eng
publication: 'Proceedings of the 2018 Conference of the North American Chapter of the
Association for Computational Linguistics: Human Language Technologies,
Volume 1 (Long Papers)'
publication_status: published
publisher: Association for Computational Linguistics
status: public
title: 'The Argument Reasoning Comprehension Task: Identification and Reconstruction
of Implicit Warrants'
type: conference
user_id: '15504'
year: '2018'
...
---
_id: '48302'
author:
- first_name: Ivan
full_name: Habernal, Ivan
id: '101881'
last_name: Habernal
- first_name: Henning
full_name: Wachsmuth, Henning
last_name: Wachsmuth
- first_name: Iryna
full_name: Gurevych, Iryna
last_name: Gurevych
- first_name: Benno
full_name: Stein, Benno
last_name: Stein
citation:
ama: 'Habernal I, Wachsmuth H, Gurevych I, Stein B. Before Name-Calling: Dynamics
and Triggers of Ad Hominem Fallacies in Web Argumentation. In: Proceedings
of the 2018 Conference of the North American Chapter of the Association
for Computational Linguistics: Human Language Technologies, Volume 1
(Long Papers). Association for Computational Linguistics; 2018. doi:10.18653/v1/n18-1036'
apa: 'Habernal, I., Wachsmuth, H., Gurevych, I., & Stein, B. (2018). Before
Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in Web
Argumentation. 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/n18-1036'
bibtex: '@inproceedings{Habernal_Wachsmuth_Gurevych_Stein_2018, title={Before Name-Calling:
Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation},
DOI={10.18653/v1/n18-1036},
booktitle={Proceedings of the 2018 Conference of the North American Chapter of
the Association for Computational Linguistics: Human Language
Technologies, Volume 1 (Long Papers)}, publisher={Association for Computational
Linguistics}, author={Habernal, Ivan and Wachsmuth, Henning and Gurevych, Iryna
and Stein, Benno}, year={2018} }'
chicago: 'Habernal, Ivan, Henning Wachsmuth, Iryna Gurevych, and Benno Stein. “Before
Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in Web
Argumentation.” In Proceedings of the 2018 Conference of the North American
Chapter of the Association for Computational Linguistics: Human Language
Technologies, Volume 1 (Long Papers). Association for Computational
Linguistics, 2018. https://doi.org/10.18653/v1/n18-1036.'
ieee: 'I. Habernal, H. Wachsmuth, I. Gurevych, and B. Stein, “Before Name-Calling:
Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation,”
2018, doi: 10.18653/v1/n18-1036.'
mla: 'Habernal, Ivan, et al. “Before Name-Calling: Dynamics and Triggers of Ad Hominem
Fallacies in Web Argumentation.” Proceedings of the 2018 Conference
of the North American Chapter of the Association for Computational Linguistics:
Human Language Technologies, Volume 1 (Long Papers), Association
for Computational Linguistics, 2018, doi:10.18653/v1/n18-1036.'
short: 'I. Habernal, H. Wachsmuth, I. Gurevych, B. Stein, in: Proceedings of the
2018 Conference of the North American Chapter of the Association for
Computational Linguistics: Human Language Technologies, Volume 1 (Long
Papers), Association for Computational Linguistics, 2018.'
date_created: 2023-10-19T08:29:28Z
date_updated: 2023-10-19T12:09:55Z
department:
- _id: '34'
- _id: '820'
doi: 10.18653/v1/n18-1036
language:
- iso: eng
publication: 'Proceedings of the 2018 Conference of the North American Chapter of the
Association for Computational Linguistics: Human Language Technologies,
Volume 1 (Long Papers)'
publication_status: published
publisher: Association for Computational Linguistics
status: public
title: 'Before Name-Calling: Dynamics and Triggers of Ad Hominem Fallacies in
Web Argumentation'
type: conference
user_id: '15504'
year: '2018'
...