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Results: 164
Number of items: 164
  • Open Access
    Meng, Y., Wu, D., & Monz, C. (2025). How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise in Machine Translation. In L. Chiruzzo, A. Ritter, & L. Wang (Eds.), Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics : Proceedings of the Conference : Findings: NAACL 2025 : April 29-May 4, 2025 (pp. 7466–7482). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-naacl.416
  • Open Access
    Vasilev, S., Herold, C., Liao, B., Hashemi, S. H., Khadivi, S., & Monz, C. (2025). Unilogit: Robust Machine Unlearning for LLMs Using Uniform-Target Self-Distillation. In W. Che, J. Nabende, E. Shutova, & M. T. Pilehvar (Eds.), The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025) : Findings of the Association for Computational Linguistics: ACL 2025: ACL 2025 : July 27-August 1, 2025 (pp. 22453-22472). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-acl.1154
  • Open Access
    Liao, B., Herold, C., Hashemi, S. H., Vasilev, S., Khadivi, S., & Monz, C. (2025). ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning. In W. Che, J. Nabende, E. Shutova, & M. T. Pilehvar (Eds.), The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025) : Findings of the Association for Computational Linguistics: ACL 2025: ACL 2025 : July 27-August 1, 2025 (pp. 24779-24804). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-acl.1272
  • Open Access
    Wu, D., Meng, Y., Nachesa, M., Aycock, S., & Monz, C. (2025). UvA-MT's Participation in the WMT25 General Translation Shared Task. In B. Haddow, T. Kocmi, P. Koehn, & C. Monz (Eds.), Tenth Conference on Machine Translation : Proceedings of the Conference: WMT 2025 : November 8-9, 2025 (pp. 688-694). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.wmt-1.45
  • Open Access
    Tan, S., Wu, D., Stap, D., Aycock, S., & Monz, C. (2024). UvA-MT’s Participation in the WMT24 General Translation Shared Task. In B. Haddow, T. Kocmi, P. Koehn, & C. Monz (Eds.), Ninth Conference on Machine Translation : Proceedings of the Conference: WMT 2024 : November 15-16, 2024 (pp. 176-184). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.wmt-1.11
  • Open Access
    Meng, Y., & Monz, C. (2024). Disentangling the Roles of Target-side Transfer and Regularization in Multilingual Machine Translation. In Y. Graham, & M. Purver (Eds.), The 18th Conference of the European Chapter of the Association for Computational Linguistics : Proceedings of the Conference: EACL 2024 : March 17-22, 2024 (Vol. 1, pp. 1828–1840). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.eacl-long.110
  • Open Access
    Naszádi, K., Oliehoek, F. A., & Monz, C. (2024). Communicating with Speakers and Listeners of Different Pragmatic Levels. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), The 2024 Conference on Empirical Methods in Natural Language Processing : Proceedings of the Conference: EMNLP 2024 : November 12-16, 2024 (pp. 21777-21783). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.1213
  • Open Access
    Wu, D., Tan, S., Meng, Y., Stap, D., & Monz, C. (2024). How Far can 100 Samples Go? Unlocking Zero-Shot Translation with Tiny Multi-Parallel Data. In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), The 62nd Annual Meeting of the Association for Computational Linguistics : Findings of the Association for Computational Linguistics: ACL 2024: ACL 2024 : August 11-16, 2024 (pp. 15092-15108). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-acl.896
  • Open Access
    Wu, D., Lei, Y., Yates, A., & Monz, C. (2024). Representational Isomorphism and Alignment of Multilingual Large Language Models. In J. Sälevä, & A. Owodunni (Eds.), The 4th Workshop on Multilingual Representation Learning : proceedings of the workshop: MRL 2024 : November 16, 2024 (pp. 293-297). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.mrl-1.24
  • Open Access
    Chen, X., Liao, B., Qi, J., Eustratiadis, P., Monz, C., Bisazza, A., & de Rijke, M. (2024). The SIFo Benchmark: Investigating the Sequential Instruction Following Ability of Large Language Models. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), The 2024 Conference on Empirical Methods in Natural Language Processing : Findings of EMNLP 2024: EMNLP 2024 : November 12-16, 2024 (pp. 1691-1706). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-emnlp.92
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