Isabelle Augenstein

Isabelle Augenstein

Professor

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    ORCID: 0000-0003-1562-7909

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

    2. 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 journal › Journal article › Research › peer-review

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

    4. Published

      Multi-Sense Language Modelling

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

      Research output: Contribution to journal › Journal article › Research

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

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

    7. Published

      Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing

      Kaffee, L., Arora, Arnav, Talat, Z. & Augenstein, Isabelle, 2023, Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics (ACL), p. 13977-13998

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

    8. 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

    9. Published

      Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing

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

      Research output: Contribution to journal › Journal article › Research › peer-review

    10. Published

      Combining Sentiment Lexica with a Multi-View Variational Autoencoder

      Hoyle, A. M., Wolf-sonkin, L., Wallach, H., Cotterell, R. & Augenstein, Isabelle, 2019, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, p. 635-640

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

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