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Dr. W. (Wilker) Ferreira Aziz

Faculty of Science
ILLC
Visiting address
  • Science Park 900
  • Room number: L6.55
Postal address
  • Postbus 94242
    1090 GE Amsterdam
Contact details
  • Publications

    2025

    • Lobato Ferreira, P. M., Titov, I. A., & Ferreira Aziz, W. (2025). Explanation Regularisation through the Lens of Attributions. In Proceedings of the 31st International Conference on Computational Linguistics (pp. 6530–6551). Association for Computational Linguistics. https://aclanthology.org/2025.coling-main.436/

    2024

    • Baan, J., Fernández, R., Plank, B., & Aziz, W. (2024). Interpreting Predictive Probabilities: Model Confidence or Human Label Variation? In Y. Graham, M. Purver, & M. Purver (Eds.), EACL 2024 - 18th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference (Vol. 2, pp. 268-277). Association for Computational Linguistics (ACL).
    • Ilia, E., & Ferreira Aziz, W. (2024). Predict the Next Word: <Humans exhibit uncertainty in this task and language models _____> In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics Association for Computational Linguistics (ACL). https://aclanthology.org/2024.eacl-short.22/
    • Ilia, E., & Ferreira Aziz, W. (2024). Variability Need Not Imply Error: The Case of Adequate but Semantically Distinct Responses. Manuscript submitted for publication. In ACL Rolling Review

    2023

    2022

    • 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 [details]
    • Eikema, B., & Aziz, W. (2022). Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translation. 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. 10978-10993). Association for Computational Linguistics. https://doi.org/10.48550/arXiv.2108.04718, https://doi.org/10.18653/v1/2022.emnlp-main.754 [details]
    • Farinhas, A., Ferreira Aziz, W., Niculae, V., & Martins, A. F. T. (2022). Sparse Communication via Mixed Distributions. In International Conference on Learning Representations https://openreview.net/forum?id=WAid50QschI

    2021

    2020

    • Ataman, D., Ferreira Aziz, W., & Birch, A. (2020). A Latent Morphology Model for Open-Vocabulary Neural Machine Translation. In International Conference on Learning Representations https://openreview.net/pdf?id=BJxSI1SKDH
    • 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 [details]
    • 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 [details]
    • 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 [details]

    2019

    • 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 [details]
    • Calixto, I., Rios, M., & Aziz, W. (2019). Latent Variable Model for Multi-modal Translation. 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. 6392–6405). The Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1642 [details]
    • De Cao, N., Aziz, W., & Titov, I. (2019). Question answering by reasoning across documents with graph convolutional networks. In J. Burstein, C. Doran, & T. Solorio (Eds.), The 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: NAACL HLT 2019 : proceedings of the conference : June 2-June 7, 2019 (Vol. 1, pp. 2306-2317). The Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1240 [details]
    • De Cao, N., Ferreira Aziz, W., & Titov, I. A. (2019). Block Neural Autoregressive Flow. In Proceedings of the the 35th Uncertainty in Artificial Intelligence Conference AUAI Press. http://auai.org/uai2019/proceedings/papers/511.pdf
    • Eikema, B., & Aziz, W. (2019). Auto-Encoding Variational Neural Machine Translation. In I. Augenstein, S. Gella, S. Ruder, K. Kann, J. Welbl, A. Conneau, X. Ren, & M. Rei (Eds.), The 4th Workshop on Representation Learning for NLP (RepL4NLP-2019): ACL 2019 : proceedings of the workshop : August 2, 2019, Florence, Italy (pp. 124–141). The Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-4315 [details]

    2018

    • Rios, M., Aziz, W., & Sima'an, K. (2018). Deep Generative Model for Joint Alignment and Word Representation. In M. Walker, H. Ji, & A. Stent (Eds.), NAACL-HLT 2018 : The 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: proceedings of the conference : June 1-June 6, 2018, New Orleans, Louisiana (Vol. 1, pp. 1011-1023). The Association for Computational Linguistics. https://doi.org/10.18653/v1/N18-1092 [details]
    • Schulz, P., Aziz, W., & Cohn, T. (2018). A Stochastic Decoder for Neural Machine Translation. In I. Gurevych, & Y. Miyao (Eds.), ACL 2018 : The 56th Annual Meeting of the Association for Computational Linguistics: proceedings of the conference : July 15-20, 2018, Melbourne, Australia (Vol. 1, pp. 1243-1252). The Association for Computational Linguistics. https://doi.org/10.18653/v1/P18-1115 [details]

    2017

    • Bastings, J., Titov, I., Aziz, W., Marcheggiani, D., & Sima'an, K. (2017). Graph Convolutional Encoders for Syntax-aware Neural Machine Translation. In M. Palmer, R. Hwa, & S. Riedel (Eds.), The Conference on Empirical Methods in Natural Language Processing: proceedings of the conference : EMNLP 2017 : September 9-11, 2017, Copenhagen, Denmark (pp. 1957-1967). Association for Computational Linguistics. https://doi.org/10.18653/v1/D17-1209 [details]

    2016

    • Daiber, J., Stanojević, M., Aziz, W., & Sima'an, K. (2016). Examining the Relationship between Preordering and Word Order Freedom in Machine Translation. In Proceedings of the First Conference on Machine Translation: Berlin, Germany, August 11-12, 2016 (Vol. 1, pp. 118-130). Association for Computational Linguistics. https://doi.org/10.18653/v1/W16-2213 [details]
    • Schulz, P., & Aziz, W. (2016). Fast Collocation-Based Bayesian HMM Word Alignment. In Y. Matsumoto, & R. Prasad (Eds.), Proceedings of COLING 2016: technical papers: the 26th International Conference on Computational Linguistics : Osaka, Japan, December 11-17 2016 (pp. 3146-3155). The COLING 2016 Organizing Committee. http://www.aclweb.org/anthology/C/C16/C16-1296 [details]
    • Schulz, P., Aziz, W., & Sima'an, K. (2016). Word Alignment without NULL words. In K. Erk, & N. A. Smith (Eds.), The 54th Annual Meeting of the Association for Computational Linguistics : ACL 2016: proceedings of the conference : August 7-12, 2016, Berlin Germany (Vol. 2, pp. 169-174). Association for Computational Linguistics. https://doi.org/10.18653/v1/P16-2028 [details]

    2015

    2014

    • Aziz, W., Dymetman, M., & Specia, L. (2014). Exact Decoding for Phrase-Based Statistical Machine Translation. In A. Moschitti, B. Pang, & W. Daelemans (Eds.), EMNLP 2014: the 2014 Conference on Empirical Methods In Natural Language Processing: proceedings of the conference: October 25-29, 2014, Doha, Qatar (pp. 1237-1249). Association for Computational Linguistics. http://www.aclweb.org/anthology/D14-1131 [details]

    2020

    • De Cao, N., & Ferreira Aziz, W. (2020). The Power Spherical distribution. Paper presented at ICML 2020 workshop INNF+: Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models. https://arxiv.org/abs/2006.04437

    Prize / grant

    • Eikema, B. & Aziz, W. (2020). COLING Best Paper Award.

    2024

    • Bhargav, S. (2024). Navigating uncertain waters in information retrieval: Adapting to domain shifts and complex information needs. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
    • De Cao, N. (2024). Entity centric neural models for natural language processing. [Thesis, fully internal, Universiteit van Amsterdam]. [details]

    2023

    • Abnar, S. (2023). Inductive biases for learning natural language. [Thesis, fully internal, Universiteitsbibliotheek]. [details]

    2020

    • 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. [details]
    • Del Tredici, M. (2020). Linguistic variation in online communities: A computational perspective. [Thesis, fully internal, Universiteit van Amsterdam]. Institute for Logic, Language and Computation. [details]
    • 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. [details]

    2018

    • Daiber, J. (2018). Typologically robust statistical machine translation: Understanding and exploiting differences and similarities between languages in machine translation. [Thesis, fully internal, Universiteit van Amsterdam]. [details]

    2017

    • Stanojević , M. (2017). Permutation forests for modeling word order in machine translation. [Thesis, fully internal, Universiteit van Amsterdam]. [details]

    2023

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