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Results: 91
Number of items: 91
  • Open Access
    Wang, B., Lapata, M., & Titov, I. (2021). Learning from Executions for 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. 2747-2759). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-main.219
  • Open Access
    Zhang, B., Titov, I., & Sennrich, R. (2021). Sparse Attention with Linear Units. In 2021 Conference on Empirical Methods in Natural Language Processing: EMNLP 2021 : proceedings of the conference : November 7-11, 2021 (pp. 6507-6520). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.523
  • 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
    Zhao, Y., & Titov, I. (2021). An empirical study of compound PCFGs. In E. Ben-David, S. Cohen, R. McDonald, B. Plank, R. Reichart, G. Rotman, & Y. Ziser (Eds.), The Second Workshop on Domain Adaptation for NLP: Adap-NLP 2021 : Proceedings of the Workshop : April 20, 2021 (pp. 166-171). Association for Computational Linguistics. https://aclanthology.org/2021.adaptnlp-1.17
  • Open Access
    Baziotis, C., Titov, I., Birch, A., & Haddow, B. (2021). Exploring Unsupervised Pretraining Objectives for Machine Translation. 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. 2956-2971). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.findings-acl.261
  • Open Access
    Bražinskas, A., Lapata, M., & Titov, I. (2021). Learning Opinion Summarizers by Selecting Informative Reviews. 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. 9424-9442). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.743
  • Open Access
    Zhang, B., Titov, I., Haddow, B., & Sennrich, R. (2021). Beyond sentence-level end-to-end speech translation: Context helps. 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. 2566-2578). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.acl-long.200
  • 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
    Wang, Y., Che, W., Titov, I., Cohen, S. B., Lei, Z., & Liu, T. (2021). A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples. 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. 2344-2354). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.findings-acl.207
  • Open Access
    Lyu, C., Cohen, S. B., & Titov, I. (2021). A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing. 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. 9075-9091). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.714
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