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Results: 34
Number of items: 34
  • Open Access
    Baan, J., Aziz, W., Plank, B., & Fernández, R. (2022). Stop Measuring Calibration When Humans Disagree. In Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: December 7-11, 2022, Abu Dhabi, United Arab Emirates (pp. 1892–1915). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.124
  • 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
    Correia, G., Niculae, V., Aziz, W., & Martins, A. (2021). Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 15, pp. 11789-11802). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/887caadc3642e304ede659b734f79b00-Abstract.html
  • 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
    Eikema, B., & Aziz, W. (2020). Is MAP Decoding All You Need? The Inadequacy of the Mode in 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. 4506–4520). International Committee on Computational Linguistics. https://doi.org/10.18653/v1/2020.coling-main.398
  • Open Access
    Del Tredici, M. (2020). Linguistic variation in online communities: A computational perspective. [Thesis, fully internal, Universiteit van Amsterdam]. Institute for Logic, Language and Computation.
  • 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
    Pelsmaeker, T., & Aziz, W. (2020). Effective Estimation of Deep Generative Language Models. 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. 7220-7236). The Association for Computational Linguistics. http://10.18653/v1/2020.acl-main.646
  • 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
    Schulz, P. (2020). Latent variable models for machine translation and how to learn them. [Thesis, fully internal, Universiteit van Amsterdam]. Institute for Logic, Language and Computation.
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