Isabelle Augenstein

Isabelle Augenstein

Associate Professor


Publication year:
  1. Published

    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. (eds.). PMLR, p. 440-449 (Proceedings of Machine Learning Research, Vol. 124).

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

  2. Published

    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, p. 1256-1262

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

  3. Published

    Learning what to share between loosely related tasks

    Ruder, S., Bingel, J., Augenstein, Isabelle & Søgaard, Anders, 23 May 2017, In: arXiv.

    Research output: Contribution to journalJournal articleResearch

  4. Published

    Latent Multi-Task Architecture Learning

    Ruder, S., Bingel, J., Augenstein, Isabelle & Søgaard, Anders, 2019, Proceedings of 33nd AAAI Conference on Artificial Intelligence, AAAI 2019. AAAI Press, p. 4822-4829

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

  5. Published

    White Paper - Creating a Repository of Objectionable Online Content: Addressing Undesirable Biases and Ethical Considerations

    Solorio, T., Shafaei, M., Smailis, C., Augenstein, Isabelle, Mitchell, M., Stapf, I. & Kakadiaris, I., 2021, In: OpenReview.net. 5 p.

    Research output: Contribution to journalJournal articleResearch

  6. Published

    Nightmare at test time: How punctuation prevents parsers from generalizing

    Søgaard, Anders, Lhoneux, M. D. & Augenstein, Isabelle, 2018, Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP . Association for Computational Linguistics, p. 25–29

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

  7. Published

    Disembodied Machine Learning: On the Illusion of Objectivity in NLP

    Waseem, Z., Lulz, S., Bingel, J. & Augenstein, Isabelle, 2020, In: OpenReview.net. 7 p.

    Research output: Contribution to journalJournal articleResearch

  8. Published

    Jack the Reader – A Machine Reading Framework

    Weissenborn, D., Minervini, P., Dettmers, T., Augenstein, Isabelle, Welbl, J., Rocktäschel, T., Bošnjak, M., Mitchell, J., Demeester, T., Stenetorp, P. & Riedel, S., 2018, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics-System Demonstrations. Association for Computational Linguistics, p. 25–30

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

  9. Published

    Claim Check-Worthiness Detection as Positive Unlabelled Learning

    Wright, Dustin & Augenstein, Isabelle, 2020, Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, p. 476-488

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

  10. Published

    Transformer Based Multi-Source Domain Adaptation

    Wright, Dustin & Augenstein, Isabelle, 2020, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, p. 7963-7974

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

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