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Results: 163
Number of items: 163
  • 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
    Naszádi, K., Manggala, P., & Monz, C. (2023). Aligning Predictive Uncertainty with Clarification Questions in Grounded Dialog. 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. 14988–14998). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.999
  • Open Access
    Wu, D., & Monz, C. (2023). Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine Translation. In H. Bouamor, J. Pino, & K. Bali (Eds.), The 2023 Conference on Empirical Methods in Natural Language Processing: EMNLP 2023 : Proceedings of the Conference : December 6-10, 2023 (pp. 9749–9764). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.605
  • Open Access
    Stap, D., & Monz, C. (2023). Multilingual k-Nearest-Neighbor Machine Translation. In H. Bouamor, J. Pino, & K. Bali (Eds.), The 2023 Conference on Empirical Methods in Natural Language Processing: EMNLP 2023 : Proceedings of the Conference : December 6-10, 2023 (pp. 9200–9208). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.571
  • Open Access
    Soleimani, A., Monz, C., & Worring, M. (2023). NonFactS: NonFactual Summary Generation for Factuality Evaluation in Document Summarization. In A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), Findings of the Association for Computational Linguistics: ACL 2023: July 9-14, 2023 (pp. 6405-6419). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-acl.400
  • Open Access
    Wu, D., Tan, S., Stap, D., Araabi, A., & Monz, C. (2023). UvA-MT’s Participation in the WMT 2023 General Translation Shared Task. In P. Koehn, B. Haddow, T. Kocmi, & C. Monz (Eds.), Eighth Conference on Machine Translation: WMT 2023 : December 6-7, 2023 (pp. 175–180). Association for Computational Linguistics. https://doi.org/10.48550/arXiv.2310.09946, https://doi.org/10.18653/v1/2023.wmt-1.17
  • 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
  • Open Access
    Liao, B., Tan, S., & Monz, C. (2023). Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning. In Thirty-seventh Annual Conference on Neural Information Processing Systems OpenReview. https://openreview.net/forum?id=J8McuwS3zY
  • 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
    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
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