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Results: 1,033
Number of items: 1,033
  • Open Access
    Li, D. (2020). Effective collection construction for information retrieval evaluation and optimization. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Vakulenko, S., Savenko, V., & de Rijke, M. (2020). Conversational Browsing. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2012.03704
  • Open Access
    Dakwale, P. (2020). Strategies for effective utilization of training data for machine translation. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Bleeker, M., & de Rijke, M. (2020). Bidirectional Scene Text Recognition with a Single Decoder. In G. De Giacomo, A. Catala, B. Dilkina, M. Milano, S. Barro, A. Bugarín, & J. Lang (Eds.), ECAI 2020: 24th European Conference on Artificial Intelligence : 29 August-8 September 2020, Santiago de Compostela, Spain, including 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) : proceedings (pp. 2664–2671). (Frontiers in Artificial Intelligence and Applications; Vol. 325). IOS Press. https://doi.org/10.3233/FAIA200404
  • Open Access
    Ghader, H. (2020). An empirical analysis of phrase-based and neural machine translation. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Oosterhuis, H. R. (2020). Learning from user interactions with rankings: A unification of the field. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Lin, Y., Ren, P., Chen, Z., Ren, Z., Ma, J., & de Rijke, M. (2020). Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation. IEEE Transactions on Knowledge & Data Engineering, 32(8), 1502-1516. https://doi.org/10.1109/TKDE.2019.2906190
  • Open Access
    ter Hoeve, M., Kiseleva, Y., & de Rijke, M. (2020). What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization. (v1 ed.) ArXiv. https://doi.org/https://arxiv.org/abs/2012.07619v1
  • Open Access
    Li, Z. (2020). Optimizing interactive systems with data-driven objectives. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Li, Z., Lee, S., Peng, B., Li, J., Kiseleva, J., de Rijke, M., Shayandeh, S., & Gao, J. (2020). Guided Dialogue Policy Learning without Adversarial Learning in the Loop. 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. 2308–2317). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.findings-emnlp.209
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