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Results: 90
Number of items: 90
  • 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
    Bražinskas, A., Lapata, M., & Titov, I. (2020). Unsupervised opinion summarization as copycat-review generation. In D. Jurafsky, J. Chai, N. Schluter, & J. Tetreault (Eds.), The 58th Annual Meeting of the Association for Computational Linguistics: ACL 2020 : Proceedings of the Conference : July 5-10, 2020 (pp. 5151-5169). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.461
  • Open Access
    Bastings, J. (2020). A tale of two sequences: Interpretable and linguistically-informed deep learning for natural language processing. [Thesis, fully internal, Universiteit van Amsterdam]. Institute for Logic, Language and Computation.
  • Open Access
    De Cao, N., Schlichtkrull, M., Aziz, W., & Titov, I. (2020). How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking. 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. 3243–3255). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.262
  • 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
  • Open Access
    Kipf, T. N. (2020). Deep learning with graph-structured representations. [Thesis, fully internal, Universiteit van Amsterdam].
  • Wang, B., Titov, I., & Lapata, M. (2019). Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs. In K. Inui, J. Jiang, V. Ng, & X. Wan (Eds.), 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing: EMNLP-IJCNLP 2019 : proceedings of the conference : November 3-7, 2019, Hong Kong, China (pp. 3774-3785). The Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1391
  • Open Access
    Bastings, J., Aziz, W., & Titov, I. (2019). Interpretable Neural Predictions with Differentiable Binary Variables. In A. Korhonen, D. Traum, & L. Màrquez (Eds.), The 57th Annual Meeting of the Association for Computational Linguistics: ACL 2019 : proceedings of the conference : July 28-August 2, 2019, Florence, Italy (pp. 2963-2977). The Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1284
  • Open Access
    Emelin, D., Titov, I., & Sennrich, R. (2019). Widening the representation bottleneck in neural machine translation with lexical shortcuts. In O. Bojar, R. Chatterjee, C. Federmann, M. Fishel, Y. Graham, B. Haddow, M. Huck, A. Jimeno Yepes, P. Koehn, A. Martins, C. Monz, M. Negri, A. Névéol, M. Neves, M. Post, M. Turchi, & K. Verspoor (Eds.), Fourth Conference on Machine Translation - Proceedings of the Conference: WMT 2019 (Vol. 1, pp. 102-115). Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-5211
  • Open Access
    Lyu, C., Cohen, S. B., & Titov, I. (2019). Semantic Role Labeling with Iterative Structure Refinement. In K. Inui, J. Jiang, V. Ng, & X. Wan (Eds.), 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing: EMNLP-IJCNLP 2019 : proceedings of the conference : November 3-7, 2019, Hong Kong, China (pp. 1071-1082). The Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1099
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