Isabelle Augenstein

Isabelle Augenstein

Professor

Medlem af:

    ORCID: 0000-0003-1562-7909

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    1. 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

    2. Udgivet

      Multi-Hop Fact Checking of Political Claims

      Ostrowski, W., Arora, Arnav, Atanasova, Pepa Kostadinova & Augenstein, Isabelle, 2021, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence, Bind CoRR 2020. s. 3892-3898 (arXiv.org).

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

    3. 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

    4. 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

    5. 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

    6. Udgivet

      TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP

      Rethmeier, Nils, Saxena, V. K. & Augenstein, Isabelle, 2020, Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAII). Peters, J. & Sontag, D. (red.). PMLR, s. 440-449 (Proceedings of Machine Learning Research, Bind 124).

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

    7. Udgivet

      A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned, and Perspectives

      Rethmeier, Nils & Augenstein, Isabelle, 2023, I: ACM Computing Surveys. 55, 10, 17 s., 203.

      Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

    8. Udgivet

      What Can We Do to Improve Peer Review in NLP?

      Rogers, A. & Augenstein, Isabelle, 2020, Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, s. 1256-1262

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

    9. Udgivet

      QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension

      Rogers, Anna, Gardner, M. & Augenstein, Isabelle, 2023, I: ACM Computing Surveys. 55, 10, 45 s., 197.

      Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

    10. Udgivet

      Learning what to share between loosely related tasks

      Ruder, S., Bingel, J., Augenstein, Isabelle & Søgaard, Anders, 23 maj 2017, I: arXiv.

      Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning

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