Isabelle Augenstein

Isabelle Augenstein

Professor

Medlem af:

    ORCID: 0000-0003-1562-7909

    Udgivelsesår:
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    1. Udgivet

      Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing

      Jolly, S., Atanasova, Pepa Kostadinova & Augenstein, Isabelle, 2022, I: Information (Switzerland). 13, 10, s. 1-18 500.

      Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

    2. Udgivet

      Quantifying gender biases towards politicians on Reddit

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

      Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

    3. Udgivet

      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), s. 11670–11688

      Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

    4. Udgivet

      Machine Reading, Fast and Slow: When Do Models “Understand” Language?

      Ray Choudhury, S., Rogers, Anna & Augenstein, Isabelle, 2022, Proceedings of the 29th International Conference on Computational Linguistics. Association for Computational Linguistics (ACL), s. 78–93

      Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

    5. Udgivet

      Can Edge Probing Tests Reveal Linguistic Knowledge in QA Models?

      Ray Choudhury, S., Bhutani, N. & Augenstein, Isabelle, 2022, Proceedings of the 29th International Conference on Computational Linguistics. Association for Computational Linguistics (ACL), s. 1620–1635

      Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

    6. Udgivet

      Long-Tail Zero and Few-Shot Learning via Contrastive Pretraining on and for Small Data

      Rethmeier, Nils & Augenstein, Isabelle, 2022, I: Computer Sciences & Mathematics Forum . 3, 18 s., 10.

      Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

    7. Udgivet

      A Neighborhood Framework for Resource-Lean Content Flagging

      Sarwar, S. M., Zlatkova, D., Hardalov, M., Dinkov, Y., Augenstein, Isabelle & Nakov, P., 2022, I: Transactions of the Association for Computational Linguistics. 10, s. 484-502

      Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

    8. Udgivet

      Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection

      Sen, I., Samory, M., Wagner, C. & Augenstein, Isabelle, 2022, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics (ACL), s. 4716-4726

      Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

    9. Udgivet

      A Latent-Variable Model for Intrinsic Probing

      Stanczak, Karolina Ewa, Hennigen, L. T., Williams, A., Cotterell, R. & Augenstein, Isabelle, 20 jan. 2022, arxiv.org, 15 s.

      Publikation: Working paper › Preprint › Forskning

    10. Udgivet

      Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Models

      Stanczak, Karolina Ewa, Ponti, E., Hennigen, L. T., Cotterell, R. & Augenstein, Isabelle, 2022, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics (ACL), s. 1589-1598

      Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

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    ID: 180388519