Publications
- 2021
- Timo Schick and Hinrich Schütze (2021). Generating Datasets with Pretrained Language Models. arXiv preprint, arXiv:2104.07540. (Code)
- Timo Schick, Sahana Udupa and Hinrich Schütze (2021). Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP. arXiv preprint, arXiv:2103.00453. (Code)
- Timo Schick and Hinrich Schütze (2021). It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners. In Proceedings of the 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). (Code)
- Timo Schick and Hinrich Schütze (2021). Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL). (Code)
- 2020
- Timo Schick and Hinrich Schütze (2020). Few-Shot Text Generation with Pattern-Exploiting Training. arXiv preprint, arXiv:2012.11926. (Code)
- Timo Schick, Helmut Schmid and Hinrich Schütze (2020). Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification. In Proceedings of the 28th International Conference on Computational Linguistics (COLING). (Code)
- Timo Schick and Hinrich Schütze (2020). BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance. In Proceedings of the 2020 Annual Conference of the Association for Computational Linguistics (ACL). (Code)
- Timo Schick and Hinrich Schütze (2020). Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence. (Code)
- 2019
- Timo Schick and Hinrich Schütze (2019). Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts. In Proceedings of the Seventeenth Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). (Code)
- Timo Schick and Hinrich Schütze (2019). Learning Semantic Representations for Novel Words: Leveraging Both Form and Context. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence. (Code)
- 2017
- Timo Schick (2017). Transition-based generation from abstract meaning representations. Master's thesis. (Code)