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Results: 1,033
Number of items: 1,033
  • Open Access
    Zheng, J., Cai, F., Chen, H., & de Rijke, M. (2020). Pre-train, Interact, Fine-tune: A Novel Interaction Representation for Text Classification. Information Processing & Management, 57(6), Article 102215. https://doi.org/10.1016/j.ipm.2020.102215
  • Open Access
    Pei, J., Ren, P., Monz, C., & de Rijke, M. (2020). Retrospective and Prospective Mixture-of-Generators for Task-oriented Dialogue Response Generation. 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. 2148-2155). ( Frontiers in Artificial Intelligence and Applications; Vol. 325). IOS Press. https://doi.org/10.3233/FAIA200339
  • Open Access
    Oosterhuis, H., & de Rijke, M. (2020). Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking. In ICTIR'20: proceedings of the 2020 ACM SIGIR International Conference on Theory of Information Retrieval : September 14-17, 2020, Virtual Event, Norway (pp. 137–144). The Association for Computing Machinery. https://doi.org/10.1145/3409256.3409820
  • Open Access
    Huang, J., Oosterhuis, H., de Rijke, M., & van Hoof, H. (2020). Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems. In RECSYS 2020: 14th ACM Conference on Recommender Systems : Virtual Event, Brazil, September 22-26, 2020 (pp. 190–199). The Association for Computing Machinery. https://doi.org/10.1145/3383313.3412252
  • Open Access
    Jiang, S., Wolf, T., Monz, C., & de Rijke, M. (2020). TLDR: Token Loss Dynamic Reweighting for Reducing Repetitive Utterance. (v2 ed.) ArXiv. https://doi.org/10.48550/arXiv.2003.11963
  • Open Access
    Li, Z., Kiseleva, J., & de Rijke, M. (2020). Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems. 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. 3537–3546). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.findings-emnlp.316
  • Open Access
    Oosterhuis, H., Jagerman, R., & de Rijke, M. (2020). Unbiased Learning to Rank: Counterfactual and Online Approaches. In The Web Conference 2020: companion of the World Wide Web Conference WWW 2020 : Taipei 2020 : April 20-24, 2020, Taipei, Taiwan (pp. 299-300). International World Wide Web Conference Committee. https://doi.org/10.1145/3366424.3383107
  • Open Access
    Wu, C., Kanoulas, E., & de Rijke, M. (2020). It All Starts with Entities: A Salient Entity Topic Model. Natural Language Engineering, 26(5), 531-549. https://doi.org/10.1017/S1351324919000585
  • Open Access
    Vardasbi, A., de Rijke, M., & Markov, I. (2020). Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank. In SIGIR '20: proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval : July 25-30, 2020, virtual event, China (pp. 2089-2092). Association for Computing Machinery. https://doi.org/10.1145/3397271.3401299
  • Open Access
    Ren, P., Ren, Z., Sun, F., He, X., Yin, D., & de Rijke, M. (2020). NLP4REC: The WSDM 2020 Workshop on Natural Language Processing for Recommendations. In WSDM '20: proceedings of the 13th International Conference on Web Search and Data Mining : February 3-7, 2020, Houston, TX, USA (pp. 907-908). Association for Computing Machinery. https://doi.org/10.1145/3336191.3371884
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