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Results: 164
Number of items: 164
  • Open Access
    Tan, S., Mitani, R., Choudhary, R., Wu, Q., Sekiya, T., & Monz, C. (2026). Remedy-R: Generative Reasoning for Machine Translation Evaluation without Error Annotations. In M. Liakata, V. P. Moreira, J. Zhang, & D. Jurgens (Eds.), The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) : Findings of the Association for Computational Linguistics: ACL 2026: Findings 2026 : July 2-7, 2026 (pp. 7374–7398). Association for Computational Linguistics. https://doi.org/10.48550/arXiv.2512.18906, https://doi.org/10.18653/v1/2026.findings-acl.364
  • Open Access
    Tokarchuk, E. (2026). Representation collapse and where to find it in transformer-based neural machine translation. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Liao, B., Xu, Y., Dong, H., Li, J., Monz, C., Savarese, S., Sahoo, D., & Xiong, C. (2025). Reward-Guided Speculative Decoding for Efficient LLM Reasoning. Proceedings of Machine Learning Research, 267, 37555-37572. https://proceedings.mlr.press/v267/liao25f.html
  • Open Access
    Stap, D. (2025). Analyzing and improving cross-lingual knowledge transfer for machine translation. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Tan, S., & Monz, C. (2025). Simple Test Time Scaling for Machine Translation: Kaze-MT at the WMT25 General Translation 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. 651–656). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.wmt-1.40
  • Open Access
    Stap, D., & Monz, C. (2025). The Effect of Language Diversity When Fine-Tuning Large Language Models for Translation. In C. Christodoulopoulos, T. Chakraborty, C. Rose, & V. Peng (Eds.), The 2025 Conference on Empirical Methods in Natural Language Processing : Findings of EMNLP 2025: EMNLP 2025 : November 4-9, 2025 (pp. 4199-4211). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-emnlp.224
  • 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
    Tan, S., & Monz, C. (2025). ReMedy: Learning Machine Translation Evaluation from Human Preferences with Reward Modeling. In C. Christodoulopoulos, T. Chakraborty, C. Rose, & V. Peng (Eds.), The 2025 Conference on Empirical Methods in Natural Language Processing : Proceedings of the Conference: EMNLP 2025 : November 4-9, 2025 (pp. 4370-4387). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.emnlp-main.217
  • Open Access
    Wu, D., & Monz, C. (2025). UvA-MT at WMT25 Evaluation Task: LLM Uncertainty as a Proxy for Translation Quality. 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. 974-983). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.wmt-1.72
  • 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
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