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Results: 92
Number of items: 92
  • 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
    Zhang, B., Williams, P., Titov, I., & Sennrich, R. (2020). Improving massively multilingual neural machine translation and zero-shot translation. 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. 1628-1639). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.148
  • Open Access
    Zhang, B., Titov, I., Haddow, B., & Sennrich, R. (2020). Adaptive feature selection for end-to-end speech translation. In T. Cohn, Y. He, & Y. Liu (Eds.), Findings of the Association for Computational Linguistics : Findings of ACL: EMNLP 2020: 16-20 November, 2020 (pp. 2533-2544). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.findings-emnlp.230
  • 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
    Zhang, B., Titov, I., & Sennrich, R. (2020). Fast Interleaved Bidirectional Sequence Generation. In L. Barrault, O. Bojar, F. Bougares, R. Chatterjee, M. R. Costa-Jussà, C. Federmann, M. Fishel, A. Fraser, Y. Graham, P. Guzman, B. Haddow, M. Huck, A. Jimeno Yepes, P. Koehn, A. Martins, M. Morishita, C. Monz, M. Nagata, T. Nakazawa, & M. Negri (Eds.), Fifth Conference on Machine Translation : proceedings of the conference: EMNLP : November 19-20, 2020, online (pp. 503-518). Association for Computational Linguistics. https://aclanthology.org/2020.wmt-1.62
  • 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
    Bražinskas, A., Lapata, M., & Titov, I. (2020). Few-shot learning for opinion summarization. In B. Webber, T. Cohn, Y. Ye, & Y. Liu (Eds.), 2020 Conference on Empirical Methods in Natural Language Processing: EMNLP 2020 : proceedings of the conference : November 16-20, 2020 (pp. 4119-4135). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.337
  • Open Access
    Kipf, T. N. (2020). Deep learning with graph-structured representations. [Thesis, fully internal, Universiteit van Amsterdam].
  • 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
    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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