Isabelle Augenstein

Isabelle Augenstein

Professor

Member of:


    1. Published

      Character-level Supervision for Low-resource POS Tagging

      Kann, K., Bjerva, J., Augenstein, Isabelle, Plank, B. & Søgaard, Anders, 2018, Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP. Association for Computational Linguistics, p. 1–11

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    2. Published

      Copenhagen at CoNLL–SIGMORPHON 2018: Multilingual Inflection in Context with Explicit Morphosyntactic Decoding

      Kementchedjhieva, Yova Radoslavova, Bjerva, J. & Augenstein, Isabelle, 2018, Proceedings of the CoNLL SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection . Association for Computational Linguistics, p. 93–98

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    3. Published

      Multi-Sense Language Modelling

      Lekkas, A., Schneider-Kamp, P. & Augenstein, Isabelle, 2020, In: arXiv. CyRR 2020, 10 p.

      Research output: Contribution to journalJournal articleResearch

    4. Published

      Parameter sharing between dependency parsers for related languages

      Lhoneux, M. D., Bjerva, J., Augenstein, Isabelle & Søgaard, Anders, 2020, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, p. 4992-4997

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    5. Published

      Quantifying gender biases towards politicians on Reddit

      Marjanovic, Sara Vera, Stanczak, Karolina Ewa & Augenstein, Isabelle, 2022, In: PLoS ONE. 17, 10 October, p. 1-36 e0274317.

      Research output: Contribution to journalJournal articleResearchpeer-review

    6. Published

      Measuring Gender Bias in West Slavic Language Models

      Martinková, S., Stańczak, K. & Augenstein, Isabelle, 2023, EACL 2023 - 9th Workshop on Slavic Natural Language Processing, Proceedings of the SlavicNLP 2023. Association for Computational Linguistics (ACL), p. 146-154 9 p.

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    7. Published

      Is Sparse Attention more Interpretable?

      Meister, C., Lazov, S., Augenstein, Isabelle & Cotterell, R., 2021, Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Association for Computational Linguistics, p. 122-129

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    8. Published

      Unsupervised Evaluation for Question Answering with Transformers

      Muttenthaler, L., Augenstein, Isabelle & Bjerva, J., 2020, Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP. Association for Computational Linguistics, p. 83-90

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    9. Published

      Zero-Shot Cross-Lingual Transfer with Meta Learning

      Nooralahzadeh, F., Bekoulis, G., Bjerva, J. & Augenstein, Isabelle, 2020, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, p. 4547-4562

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    10. Published

      Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings

      Ostendorff, M., Rethmeier, Nils, Augenstein, Isabelle, Gipp, B. & Rehm, G., 2022, Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL), p. 11670–11688

      Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    ID: 180388519