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Results: 92
Number of items: 92
  • Open Access
    De Cao, N., Aziz, W., & Titov, I. (2021). Editing Factual Knowledge in Language Models. In M.-C. Moens, X. Huang, L. Specia, & S. W. Sih (Eds.), 2021 Conference on Empirical Methods in Natural Language Processing: EMNLP 2021 : proceedings of the conference : November 7-11, 2021 (pp. 6491-6506). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.522
  • Open Access
    Wang, B., Lapata, M., & Titov, I. (2021). Meta-Learning for Domain Generalization in Semantic Parsing. In K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tur, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, & Y. Zhou (Eds.), The 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: NAACL-HLT 2021 : proceedings of the conference : June 6-11, 2021 (pp. 366-379). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-main.33
  • Open Access
    De Cao, N., Aziz, W., & Titov, I. (2021). Highly Parallel Autoregressive Entity Linking with Discriminative Correction. In M.-C. Moens, X. Huang, L. Specia, & S. W. Yih (Eds.), 2021 Conference on Empirical Methods in Natural Language Processing: EMNLP 2021 : proceedings of the conference : November 7-11, 2021 (pp. 7662-7669). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.604
  • Open Access
    Conklin, H., Wang, B., Smith, K., & Titov, I. (2021). Meta-learning to compositionally generalize. In C. Zong, F. Xia, W. Li, & R. Navigli (Eds.), The 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: ACL-IJCNLP 2021 : proceedings of the conference : August 1-6, 2021 (Vol. 1, pp. 3322-3335). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.acl-long.258
  • Open Access
    Zhang, B., Titov, I., & Sennrich, R. (2021). On Sparsifying Encoder Outputs in Sequence-to-Sequence Models. In C. Zong, F. Xia, W. Li, & R. Navigli (Eds.), Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021: Findings of ACL: ACL-IJCNLP 2021 : August 1-6, 2021 (pp. 2888-2900). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.findings-acl.255
  • Open Access
    Marcheggiani, D., & Titov, I. (2020). Graph convolutions over constituent trees for syntax-aware semantic role labeling. In B. Webber, T. Cohn, Y. He, & Y. Liu (Eds.), 2020 Conference on Empirical Methods in Natural Language Processing: EMNLP 2020 : proceedings of the conference : November 16-20, 2020 (pp. 3915-3928). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.322
  • Open Access
    Hu, Z., Havrylov, S., Titov, I., & Cohen, S. B. (2020). Obfuscation for privacy-preserving syntactic parsing. In G. Bouma, Y. Matsumoto, S. Oepen, K. Sagae, D. Seddah, W. Sun, A. Søgaard, R. Tsarfaty, & D. Zeman (Eds.), The 16th International Conference on Parsing Technologies and IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies: IWPT 2020 : Proceedings of the Conference : July 9, 2020 (pp. 62-72). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.iwpt-1.7
  • Open Access
    Emelin, D., Titov, I., & Sennrich, R. (2020). Detecting word sense disambiguation biases in machine translation for model-agnostic adversarial attacks. In B. Webber, T. Cohn, Y. He, & Y. Liu (Eds.), 2020 Conference on Empirical Methods in Natural Language Processing: EMNLP 2020 : proceedings of the conference : November 16-20, 2020 (pp. 7635-7653). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.616
  • Open Access
    Zhao, Y., & Titov, I. (2020). Visually grounded compound PCFGs. In B. Webber, T. Cohn, Y. He, & Y. Liu (Eds.), 2020 Conference on Empirical Methods in Natural Language Processing: EMNLP 2020 : proceedings of the conference : November 16-20, 2020 (pp. 4369-4379). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.354
  • Open Access
    Voita, E., & Titov, I. (2020). Information-theoretic probing with minimum description length. In B. Webber, T. Cohn, Y. He, & Y. Liu (Eds.), 2020 Conference on Empirical Methods in Natural Language Processing: EMNLP 2020 : proceedings of the conference : November 16-20, 2020 (pp. 183-196). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.14
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