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Results: 12
Number of items: 12
  • 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
    Wu, D., Aycock, S., & Monz, C. (2025). Please Translate Again: Two Simple Experiments on Whether Human-Like Reasoning Helps Translation. 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. 20424-20440). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.emnlp-main.1031
  • 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
    Hu, V. T., Wu, D., Asano, Y. M., Mettes, P., Fernández-Méndez, F., Ommer, B., & Snoek, C. G. M. (2024). Flow Matching for Conditional Text Generation in a Few Sampling Steps. 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. 2, pp. 380-392). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.eacl-short.33
  • 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
    Lei, Y., Wu, D., Zhou, T., Shen, T., Cao, Y., Tao, C., & Yates, A. (2024). Meta-Task Prompting Elicits Embeddings from Large Language Models. In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) : proceedings of the conference: ACL 2024 : August 11-16, 2024 (Vol. 1, pp. 10141-10157). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.acl-long.546
  • 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
    Wu, D., Lei, Y., Yates, A., & Monz, C. (2024). Representational Isomorphism and Alignment of Multilingual 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. 14074-14085). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-emnlp.823
  • 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
    Tan, S., Wu, D., & Monz, C. (2024). Neuron Specialization: Leveraging Intrinsic Task Modularity for Multilingual Machine Translation. 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. 6506-6527). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.emnlp-main.374
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