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Results: 164
Number of items: 164
  • 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., Thulke, D., Hewavitharana, S., Ney, H., & Monz, C. (2022). Mask More and Mask Later: Efficient Pre-training of Masked Language Models by Disentangling the [MASK] Token. In Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2022: Conference on Empirical Methods in Natural Language Processing (EMNLP), Abu Dhabi, United Arab Emirates, 7-11 December 2022 (pp. 1478–1492). Association for Computational Linguistics. https://doi.org/10.48550/arXiv.2211.04898, https://doi.org/10.18653/v1/2022.findings-emnlp.106
  • Open Access
    Soleimani, A., Monz, C., & Worring, M. (2021). NLQuAD: A Non-Factoid Long Question Answering Data Set. In P. Merlo, J. Tiedemann, & R. Tsarfaty (Eds.), The 16th Conference of the European Chapter of the Association for Computational Linguistics: EACL 2021 : proceedings of the conference : April 19-23, 2021 (pp. 1245-1255). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.eacl-main.106
  • Open Access
    Ren, P., Chen, Z., Ren, Z., Kanoulas, E., Monz, C., & de Rijke, M. (2021). Conversations with Search Engines: SERP-based Conversational Response Generation. ACM Transactions on Information Systems, 39(4), Article 47. https://doi.org/10.1145/3432726
  • Open Access
    Araabi, A., & Monz, C. (2020). Optimizing Transformer for Low-Resource Neural Machine Translation. In D. Scott, N. Bel, & C. Zong (Eds.), The 28th International Conference on Computational Linguistics: COLING 2020 : Proceedings of the Conference : December 8-13, 2020, Barcelona, Spain (Online) (pp. 3429-3435). International Committee on Computational Linguistics. https://doi.org/10.18653/v1/2020.coling-main.304
  • Open Access
    Ren, P., Chen, Z., Monz, C., Ma, J., & de Rijke, M. (2020). Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation. In AAAI-20, IAAI-20, EAAI-20 proceedings: Thirty-Fourth AAAI Conference on Artificial Intelligence, Thirty-Second Conference on Innovative Applications of Artificial Intelligence, The Tenth Symposium on Educational Advances in Artificial Intelligence : February 7–12th, 2020, New York Hilton Midtown, New York, New York, USA (Vol. 5, pp. 8697-8704). (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 34). AAAI Press. https://doi.org/10.1609/aaai.v34i05.6395
  • Open Access
    Fadaee, M. (2020). Understanding and enhancing the use of context for machine translation. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Dakwale, P. (2020). Strategies for effective utilization of training data for machine translation. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Ghader, H. (2020). An empirical analysis of phrase-based and neural machine translation. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Jiang, S., Wolf, T., Monz, C., & de Rijke, M. (2020). TLDR: Token Loss Dynamic Reweighting for Reducing Repetitive Utterance. (v2 ed.) ArXiv. https://doi.org/10.48550/arXiv.2003.11963
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