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Results: 160
Number of items: 160
  • 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
  • Open Access
    Liao, B., Herold, C., Khadivi, S., & Monz, C. (2024). IKUN for WMT24 General MT Task: LLMs Are Here for Multilingual Machine Translation. 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. 263-269). Association for Computational Linguistics. https://doi.org/10.48550/arXiv.2408.11512, https://doi.org/10.18653/v1/2024.wmt-1.19
  • Open Access
    Rajaee, S., & Monz, C. (2024). Analyzing the Evaluation of Cross-Lingual Knowledge Transfer in Multilingual Language Models. 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. 2895–2914). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.eacl-long.177
  • 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
    Soleimani Bajestani, A. (2024). Advances in information verification using natural language processing. [Thesis, fully internal, Universiteit van Amsterdam].
  • Tan, S., & Monz, C. (2023). ZS-NMT-Variations, EC40 Multilingual Machine Translation Dataset/Benchmark [Data set]. GitHub. https://github.com/Smu-Tan/ZS-NMT-Variations.git
  • Open Access
    Stap, D., Niculae, V., & Monz, C. (2023). Viewing Knowledge Transfer in Multilingual Machine Translation Through a Representational Lens. In H. Bouamor, J. Pino, & K. Bali (Eds.), The 2023 Conference on Empirical Methods in Natural Language Processing : Findings of the Association for Computational Linguistics: EMNLP 2023: December 6-10, 2023 (pp. 14973–14987). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.998
  • Open Access
    Araabi, A., Niculae, V., & Monz, C. (2023). Joint Dropout: Improving Generalizability in Low-Resource Neural Machine Translation through Phrase Pair Variables. In M. Utiyama, & R. Wang (Eds.), MTS: Machine Translation Summit 2023: September 4-8, 2023, Macau SAR, China : Proceedings of Machine Translation Summit XIX. - Vol. 1: Research Track (pp. 12-25). Asia-Pacific Association for Machine Translation. https://aclanthology.org/2023.mtsummit-research.2
  • Open Access
    Liao, B., & Monz, C. (2023). Ask Language Model to Clean Your Noisy Translation Data. In H. Bouamor, J. Pino, & K. Bali (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2023: The 2023 Conference on Empirical Methods in Natural Language Processing (pp. 3215-3236). ACL. https://aclanthology.org/2023.findings-emnlp.212/
  • Open Access
    Liao, B., Meng, Y., & Monz, C. (2023). Parameter-Efficient Fine-Tuning without Introducing New Latency. In A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), The 61st Conference of the Association for Computational Linguistics: Proceedings of the Conference : ACL 2023 : July 9-14, 2023 (Vol. 1, pp. 4242–4260). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.233
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