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Number of items: 16.
Journal article
Schick, Timo; Udupa, Sahana 
ORCID: https://orcid.org/0000-0003-3647-9570 and Schütze, Hinrich
(17. December 2021):
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP.
In: Transactions of the Association for Computational Linguistics, Vol. 9: pp. 1408-1424
[PDF, 411kB]
ORCID: https://orcid.org/0000-0003-3647-9570 and Schütze, Hinrich
(17. December 2021):
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP.
In: Transactions of the Association for Computational Linguistics, Vol. 9: pp. 1408-1424
[PDF, 411kB]
Schick, Timo; Udupa, Sahana and Schütze, Hinrich
(2021):
Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP.
In: Transactions of the Association for Computational Linguistics, Vol. 9: pp. 1408-1424
Schick, Timo and Schuetze, Hinrich
(2020):
Rare Words: A Major Problem for Contextualized Embeddings and How to Fix it by Attentive Mimicking.
In: Thirty-Fourth Aaai Conference on Artificial Intelligence, the Thirty-Second Innovative Applications of Artificial Intelligence Conference and the Tenth Aaai Symposium on Educational Advances in Artificial Intelligence, Vol. 34: pp. 8766-8774
Schick, Timo and Schuetze, Hinrich
(2020):
BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance.
In: 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020): pp. 3996-4007
Schick, Timo and Schuetze, Hinrich
(2019):
Learning Semantic Representations for Novel Words: Leveraging Both Form and Context.
In: Thirty-Third Aaai Conference on Artificial Intelligence / Thirty-First Innovative Applications of Artificial Intelligence Conference / Ninth Aaai Symposium on Educational Advances in Artificial Intelligence: pp. 6965-6973
Paper
Schick, Timo; Udupa, Sahana ORCID: https://orcid.org/0000-0003-3647-9570 and Schütze, Hinrich
(2021):
Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in NLP.
arXiv
ORCID: https://orcid.org/0000-0003-3647-9570 and Schütze, Hinrich
(2021):
Self-diagnosis and self-debiasing: A proposal for reducing corpus-based bias in NLP.
arXiv
Conference or Workshop Item
Senel, Lütfi Kerem; Schick, Timo and Schütze, Hinrich
(May 2022):
CoDA21: Evaluating Language Understanding Capabilities of NLP Models With Context-Definition Alignment.
60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland.
[PDF, 290kB]
Schick, Timo and Schütze, Hinrich
(November 2021):
Few-Shot Text Generation with Natural Language Instructions.
2021 Conference on Empirical Methods in Natural Language Processing, November 7–11, 2021, Online and Punta Cana, Dominican Republic.
[PDF, 504kB]
Schick, Timo and Schütze, Hinrich
(November 2021):
Generating Datasets with Pretrained Language Models.
2021 Conference on Empirical Methods in Natural Language Processing, November 7-11, 2021, Online and Punta Cana, Dominican Republic.
[PDF, 431kB]
Schick, Timo and Schütze, Hinrich
(June 2021):
It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, June 2021, Online.
[PDF, 475kB]
Schick, Timo and Schütze, Hinrich
(April 2021):
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference.
16th Conference of the European Chapter of the Association for Computational Linguistics, April 19-23, 2021, Online.
[PDF, 493kB]
Schick, Timo; Schmid, Helmut and Schütze, Hinrich
(December 2020):
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification.
The 28th International Conference on Computational Linguistics, 8. - 11. Dezember 2020, Online.
[PDF, 275kB]
Schick, Timo and Schütze, Hinrich
(6. July 2020):
BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance.
The 58th Annual Meeting of the Association for Computational Linguistics, July 6 – 8, 2020, Seattle, USA.
[PDF, 399kB]
Schick, Timo and Schütze, Hinrich
(June 2019):
Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts.
2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2. - 7. June 2019, Minneapolis, USA.
[PDF, 304kB]
Schick, Timo and Schütze, Hinrich
(April 2019):
Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking.
The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), February 7-12, 2020, New York, USA.
[PDF, 372kB]
Schick, Timo and Schütze, Hinrich
(January 2019):
Learning Semantic Representations for Novel Words:
Leveraging Both Form and Context.
Thirty-Third AAAI Conference on Artificial Intelligence; AAAI-2019, 27. January – 01. February 2019, Honolulu, Hawaii, USA.
[PDF, 165kB]