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Results: 92
Number of items: 92
  • Open Access
    Müller-Eberstein, M., van der Goot, R., Plank, B., & Titov, I. (2023). Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training. In H. Bouamor, J. Pino, & K. Bali (Eds.), The 2023 Conference on Empirical Methods in Natural Language Processing : Findings of the Association for Computational Linguistics: EMNLP 2023: December 6-10, 2023 (pp. 13190-13208). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.879
  • Open Access
    Alukaev, D., Kiselev, S., Pershin, I., Ibragimov, B., Ivanov, V., Kornaev, A., & Titov, I. (2023). Cross-Modal Conceptualization in Bottleneck Models. In H. Bouamor, J. Pino, & K. Bali (Eds.), The 2023 Conference on Empirical Methods in Natural Language Processing: EMNLP 2023 : Proceedings of the Conference : December 6-10, 2023 (pp. 5241-5253). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.318
  • Open Access
    Zhao, Y., & Titov, I. (2023). On the Transferability of Visually Grounded PCFGs. In H. Bouamor, J. Pino, & K. Bali (Eds.), The 2023 Conference on Empirical Methods in Natural Language Processing : Findings of the Association for Computational Linguistics: EMNLP 2023: December 6-10, 2023 (pp. 7895-7910). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.530
  • Open Access
    Lindemann, M., Koller, A., & Titov, I. (2023). Compositional Generalization without Trees using Multiset Tagging and Latent Permutations. In A. Rogers, J. Boyd-Graper, & N. Okazaki (Eds.), The 61st Conference of the Association for Computational Linguistics: ACL 2023 : Proceedings of the Conference : July 9-14, 2023 (Vol. 1, pp. 14488-14506). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.810
  • Open Access
    De Cao, N., Schmid, L., Hupkes, D., & Titov, I. (2022). Sparse Interventions in Language Models with Differentiable Masking. In J. Bastings, Y. Belinkov, Y. Elazar, D. Hupkes, N. Saphra, & S. Wiegreffe (Eds.), BlackboxNLP Analyzing and Interpreting Neural Networks for NLP: BlackboxNLP 2022 : Proceedings of the Workshop : December 8, 2022 (pp. 16-27). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.blackboxnlp-1.2
  • Open Access
    Wang, B., Titov, I., Andreas, J., & Kim, Y. (2022). Hierarchical Phrase-based Sequence-to-Sequence Learning. In Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds.), The 2022 Conference on Empirical Methods in Natural Language Processing : Proceedings of the Conference: EMNLP 2022 : December 7-11, 2022 (pp. 8211-8229). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.563
  • Open Access
    Dankers, V., Lucas, C. G., & Titov, I. (2022). Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation. In S. Muresan, P. Nakov, & A. Villavicencio (Eds.), The 60th Annual Meeting of the Association for Computational Linguistics: ACL 2022 : proceedings of the conference : May 22-27, 2022 (Vol. 1, pp. 3608-3626). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.acl-long.252
  • Open Access
    Dankers, V., & Titov, I. (2022). Recursive Neural Networks with Bottlenecks Diagnose (Non-)Compositionality. In Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2022: Conference on Empirical Methods in Natural Language Processing (EMNLP), Abu Dhabi, United Arab Emirates, 7-11 December 2022 (pp. 4361–4378). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.findings-emnlp.320
  • Open Access
    Lapata, M., Titov, I., & Wang, B. (2022). Structured Reordering for Modeling Latent Alignments in Sequence Transduction. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), 35th Conference on Neural Information Processing Systems (NeurIPS 2021) : online, 6-14 December 2021 (Vol. 16, pp. 13378-13391). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2021/hash/6f46dd176364ccec308c2760189a4605-Abstract.html
  • Open Access
    Baziotis, C., Titov, I., Birch, A., & Haddow, B. (2021). Exploring Unsupervised Pretraining Objectives for Machine Translation. In C. Zong, F. Xia, W. Li, & R. Navigli (Eds.), Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021: Findings of ACL: ACL-IJCNLP 2021 : August 1-6, 2021 (pp. 2956-2971). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.findings-acl.261
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