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Recent publications

  1. Homophone Disambiguation Reveals Patterns of Context Mixing in Speech…

    Mohebbi, H., Chrupała, G., Zuidema, W., & Alishahi, A. (2023). Homophone Disambiguation Reveals Patterns of Context Mixing in Speech Transformers. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 8249–8260). Association for Computational Linguistics.
  2. Quantifying Context Mixing in Transformers

    Mohebbi, H., Zuidema, W., Chrupała, G., & Alishahi, A. (2023). Quantifying Context Mixing in Transformers. In In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (pp. 3378-3400). Association for Computational Linguistics. https://aclanthology.org/2023.eacl-main.245/
  3. Wave to Syntax - Probing spoken language models for syntax

    Shen, G., Alishahi, A., Bisazza, A., & Chrupała, G. (2023). Wave to Syntax: Probing spoken language models for syntax. In Proc. INTERSPEECH 2023 (pp. 1259-1263). (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH).
  4. Learning English with Peppa Pig

    Nikolaus, M., Alishahi, A., & Chrupała, G. (2022). Learning English with Peppa Pig. Transactions of the Association for Computational Linguistics, 10, 922-936.
  5. ZR-2021VG: Zero-Resource Speech Challenge, Visually-Grounded Language…

    Alishahi, A., Chrupała, G., Cristià, A., Dupoux, E., Higy, B., Lavechin, M., Räsänen, O., & Yu, C. (2021). ZR-2021VG: Zero-Resource Speech Challenge, Visually-Grounded Language Modelling track, 2021 edition. CoRR, abs/2107.06546. https://arxiv.org/abs/2107.06546

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