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Results: 98
Number of items: 98
  • Open Access
    Eikema, B. (2026). A sampling-based exploration of neural text generation models. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Cao, C., Zhang, Z., Bloem, J., & Sima'an, K. (2025). How Aligned Are Unimodal Language and Graph Encodings of Chemical Molecules? In K. Inui, S. Sakti, H. Wang, D. F. Wong, P. Bhattacharyya, B. Banerjee, A. Ekbal, T. Chakraborty, & D. P. Singh (Eds.), The 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: proceedings of the conference : IJCNLP-AACL 2025 : December 20-24, 2025 (Vol. 1, pp. 1084-1097). Association for Computational Linguistics. https://aclanthology.org/2025.ijcnlp-long.59/
  • Open Access
    Arnoult, S. I. (2021). Adjunction in hierarchical phrase-based translation. [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
    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.
  • Open Access
    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
  • Open Access
    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].
  • Cuong, H., & Sima'an, K. (2017). A survey of domain adaptation for statistical machine translation. Machine Translation, 31(4), 187-224. https://doi.org/10.1007/s10590-018-9216-8
  • Cuong, H., & Sima'an, K. (2017). Induction of latent domains in heterogeneous corpora: a case study of word alignment. Machine Translation, 31(4), 225-249. https://doi.org/10.1007/s10590-018-9215-9
  • Stanojević, M., & Sima'an, K. (2017). Alternative objective functions for training MT evaluation metrics. In R. Barzilay, & M.-Y. Kan (Eds.), The 55th Annual Meeting of the Association for Computational Linguistics: proceedings of the Conference : July 30-August 4, 2017, Vancouver, Canada (Vol. 2, pp. 20-25). Association for Computational Linguistics. https://doi.org/10.18653/v1/P17-2004
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