Isabelle Augenstein

Isabelle Augenstein

Professor

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    ORCID: 0000-0003-1562-7909

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    1. Published

      niversity of Copenhagen Participation in TREC Health Misinformation Track 2020: U

      Chaves Lima, L., Wright, ,. D. B., Augenstein, Isabelle & Maistro, Maria, 2020, Proceedings of the Twenty-Ninth Text REtrieval Conference (TREC 2020). Voorhees, E. M. & Ellis, A. (eds.). National Institute of Standards and Technology (NIST), 16 p. (NIST Special Publication; No. 1266).

      Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

    2. 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 proceeding › Article in proceedings › Research › peer-review

    3. Published

      X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension

      Abdou, M., Sas, C., Aralikatte, R., Augenstein, Isabelle & Søgaard, Anders, 2019, Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019). Association for Computational Linguistics, p. 265-274

      Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

    4. Published

      Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions

      Kaffee, L., Arora, Arnav & Augenstein, Isabelle, 2023, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL), p. 5891-5909

      Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

    5. 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 proceeding › Article in proceedings › Research › peer-review

    6. 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 proceeding › Article in proceedings › Research › peer-review

    7. Published

      Unsupervised Discovery of Gendered Language through Latent-Variable Modeling

      Hoyle, A. M., Wolf-sonkin, L., Wallach, H., Augenstein, Isabelle & Cotterell, R., 2020, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, p. 1706-1716

      Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

    8. Published

      Uncovering Probabilistic Implications in Typological Knowledge Bases

      Bjerva, J., Kementchedjhieva, Yova Radoslavova, Cotterell, R. & Augenstein, Isabelle, 2019, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, p. 3924–3930

      Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

    9. 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 proceeding › Article in proceedings › Research › peer-review

    10. Published

      Transductive Auxiliary Task Self-Training for Neural Multi-Task Models

      Bjerva, J., Kann, K. & Augenstein, Isabelle, 2019, Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo). Association for Computational Linguistics, p. 253–258

      Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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