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Dr. I.A. (Ivan) Titov

Faculty of Science
ILLC

Visiting address
  • Science Park 900
  • Room number: L6.52
Postal address
  • Postbus 94242
    1090 GE Amsterdam
Contact details
  • Publications

    2023

    • 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 [details]
    • Dankers, V., Titov, I., & Hupkes, D. (2023). Memorisation Cartography: Mapping out the Memorisation-Generalisation Continuum in Neural Machine Translation. 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. 8323-8343). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.518 [details]
    • Lindemann, M., Koller, A., & Titov, I. (2023). Compositional Generalisation with Structured Reordering and Fertility Layers. In A. Vlachos, & I. Augenstein (Eds.), The 17th Conference of the European Chapter of the Association for Computational Linguistics: EACL 2023 : proceedings of the conference : May 2-6, 2023 (pp. 2172–2186). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.eacl-main.159 [details]
    • 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 [details]
    • 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 [details]
    • Xu, X., Titov, I., & Lapata, M. (2023). Compositional Generalization for Data-to-Text Generation. 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. 9299-9317). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.623 [details]
    • 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 [details]
    • Züfle, M., Dankers, V., & Titov, I. (2023). Latent Feature-based Data Splits to Improve Generalisation Evaluation: A Hate Speech Detection Case Study. In D. Hupkes, V. Dankers, K. Batsuren, K. Sinha, A. Kazemnejad, C. Christodoulopoulos, R. Cotterell, & E. Bruni (Eds.), GenBench: The first workshop on generalisation (benchmarking) in NLP: GenBench 2023 : Proceedings of the Workshop : December 6, 2023 (pp. 112–129). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.genbench-1.9 [details]

    2022

    • 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 [details]
    • 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 [details]
    • 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 [details]

    2021

    • 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 [details]
    • Bražinskas, A., Lapata, M., & Titov, I. (2021). Learning Opinion Summarizers by Selecting Informative Reviews. In M.-C. Moens, X. Huang, L. Specia, & S. W. Sih (Eds.), 2021 Conference on Empirical Methods in Natural Language Processing: EMNLP 2021 : proceedings of the conference : November 7-11, 2021 (pp. 9424-9442). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.743 [details]
    • Conklin, H., Wang, B., Smith, K., & Titov, I. (2021). Meta-learning to compositionally generalize. In C. Zong, F. Xia, W. Li, & R. Navigli (Eds.), The 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: ACL-IJCNLP 2021 : proceedings of the conference : August 1-6, 2021 (Vol. 1, pp. 3322-3335). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.acl-long.258 [details]
    • De Cao, N., Aziz, W., & Titov, I. (2021). Editing Factual Knowledge in Language Models. In M.-C. Moens, X. Huang, L. Specia, & S. W. Sih (Eds.), 2021 Conference on Empirical Methods in Natural Language Processing: EMNLP 2021 : proceedings of the conference : November 7-11, 2021 (pp. 6491-6506). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.522 [details]
    • De Cao, N., Aziz, W., & Titov, I. (2021). Highly Parallel Autoregressive Entity Linking with Discriminative Correction. In 2021 Conference on Empirical Methods in Natural Language Processing: EMNLP 2021 : proceedings of the conference : November 7-11, 2021 (pp. 7662-7669). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.604
    • Lyu, C., Cohen, S. B., & Titov, I. (2021). A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing. In 2021 Conference on Empirical Methods in Natural Language Processing: EMNLP 2021 : proceedings of the conference : November 7-11, 2021 (pp. 9075-9091). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.714
    • Voita, E., Sennrich, R., & Titov, I. (2021). Analyzing the source and target contributions to predictions in neural machine translation. In C. Zong, F. Xia, W. Li, & R. Navigli (Eds.), The 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: ACL-IJCNLP 2021 : proceedings of the conference : August 1-6, 2021 (Vol. 1, pp. 1126-1140). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.acl-long.91
    • Voita, E., Sennrich, R., & Titov, I. (2021). Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT. In 2021 Conference on Empirical Methods in Natural Language Processing: EMNLP 2021 : proceedings of the conference : November 7-11, 2021 (pp. 8478-8491). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.667
    • Wang, B., Lapata, M., & Titov, I. (2021). Learning from Executions for Semantic Parsing. In K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tur, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, & Y. Zhou (Eds.), The 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: NAACL-HLT 2021 : proceedings of the conference : June 6-11, 2021 (pp. 2747-2759). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-main.219
    • Wang, B., Lapata, M., & Titov, I. (2021). Meta-Learning for Domain Generalization in Semantic Parsing. In K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tur, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, & Y. Zhou (Eds.), The 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: NAACL-HLT 2021 : proceedings of the conference : June 6-11, 2021 (pp. 366-379). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-main.33
    • Wang, B., Lapata, M., & Titov, I. (2021). Structured Reordering for Modeling Latent Alignments in Sequence Transduction. In MA. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), 35th Conference on Neural Information Processing Systems, NeurIPS 2021 (Vol. 16, pp. 13378-13391). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation.
    • Wang, Y., Che, W., Titov, I., Cohen, S. B., Lei, Z., & Liu, T. (2021). A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples. 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. 2344-2354). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.findings-acl.207
    • Zhang, B., Titov, I., & Sennrich, R. (2021). On Sparsifying Encoder Outputs in Sequence-to-Sequence Models. 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. 2888-2900). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.findings-acl.255
    • Zhang, B., Titov, I., & Sennrich, R. (2021). Sparse Attention with Linear Units. In 2021 Conference on Empirical Methods in Natural Language Processing: EMNLP 2021 : proceedings of the conference : November 7-11, 2021 (pp. 6507-6520). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.523
    • Zhang, B., Titov, I., Haddow, B., & Sennrich, R. (2021). Beyond sentence-level end-to-end speech translation: Context helps. In C. Zong, F. Xia, W. Li, & R. Navigli (Eds.), The 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: ACL-IJCNLP 2021 : proceedings of the conference : August 1-6, 2021 (Vol. 1, pp. 2566-2578). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.acl-long.200
    • Zhao, Y., & Titov, I. (2021). An empirical study of compound PCFGs. In E. Ben-David, S. Cohen, R. McDonald, B. Plank, R. Reichart, G. Rotman, & Y. Ziser (Eds.), Adapt-NLP 2021 - 2nd Workshop on Domain Adaptation for NLP, Proceedings (pp. 166-171). Association for Computational Linguistics (ACL).

    2020

    • Bražinskas, A., Lapata, M., & Titov, I. (2020). Few-shot learning for opinion summarization. In B. Webber, T. Cohn, Y. Ye, & Y. Liu (Eds.), 2020 Conference on Empirical Methods in Natural Language Processing: EMNLP 2020 : proceedings of the conference : November 16-20, 2020 (pp. 4119-4135). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.337 [details]
    • Bražinskas, A., Lapata, M., & Titov, I. (2020). Unsupervised opinion summarization as copycat-review generation. 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. 5151-5169). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.461 [details]
    • 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]
    • Emelin, D., Titov, I., & Sennrich, R. (2020). Detecting word sense disambiguation biases in machine translation for model-agnostic adversarial attacks. 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. 7635-7653). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.616 [details]
    • Hu, Z., Havrylov, S., Titov, I., & Cohen, S. B. (2020). Obfuscation for privacy-preserving syntactic parsing. In IWPT 2020 - 16th International Conference on Parsing Technologies and IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies, Proceedings of the Conference (pp. 62-72). Association for Computational Linguistics (ACL).
    • Marcheggiani, D., & Titov, I. (2020). Graph convolutions over constituent trees for syntax-aware semantic role labeling. In 2020 Conference on Empirical Methods in Natural Language Processing: EMNLP 2020 : proceedings of the conference : November 16-20, 2020 (pp. 3915-3928). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.322
    • Voita, E., & Titov, I. (2020). Information-theoretic probing with minimum description length. In 2020 Conference on Empirical Methods in Natural Language Processing: EMNLP 2020 : proceedings of the conference : November 16-20, 2020 (pp. 183-196). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.14
    • Zhang, B., Titov, I., & Sennrich, R. (2020). Fast Interleaved Bidirectional Sequence Generation. In L. Barrault, O. Bojar, F. Bougares, R. Chatterjee, M. R. Costa-Jussa, C. Federmann, M. Fishel, A. Fraser, Y. Graham, P. Guzman, B. Haddow, M. Huck, A. J. Yepes, P. Koehn, A. Martins, M. Morishita, C. Monz, M. Nagata, T. Nakazawa, & M. Negri (Eds.), 5th Conference on Machine Translation, WMT 2020 - Proceedings (pp. 503-518). Association for Computational Linguistics (ACL).
    • Zhang, B., Titov, I., Haddow, B., & Sennrich, R. (2020). Adaptive feature selection for end-to-end speech translation. 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. 2533-2544). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.findings-emnlp.230
    • Zhang, B., Williams, P., Titov, I., & Sennrich, R. (2020). Improving massively multilingual neural machine translation and zero-shot translation. In ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (pp. 1628-1639). Association for Computational Linguistics (ACL).
    • Zhao, Y., & Titov, I. (2020). Visually grounded compound PCFGs. In 2020 Conference on Empirical Methods in Natural Language Processing: EMNLP 2020 : proceedings of the conference : November 16-20, 2020 (pp. 4369-4379). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.354

    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]
    • Chen, X., Lyu, C., & Titov, I. (2019). Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks. In K. Inui, J. Jiang, V. Ng, & X. Wan (Eds.), 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing: EMNLP-IJCNLP 2019 : proceedings of the conference : November 3-7, 2019, Hong Kong, China (pp. 5415–5425). The Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1544 [details]
    • Corro, C., & Titov, I. (2019). Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder. In ICLR 2019: International Conference on Learning Representations : New Orleans, Louisiana, United States, May 6-May 9, 2019 OpenReview. https://openreview.net/forum?id=BJlgNh0qKQ [details]
    • Corro, C., & Titov, I. (2019). Learning Latent Trees with Stochastic Perturbations and Differentiable Dynamic Programming. 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. 5508–5521). The Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1551 [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
    • Emelin, D., Titov, I., & Sennrich, R. (2019). Widening the representation bottleneck in neural machine translation with lexical shortcuts. In WMT 2019 - 4th Conference on Machine Translation, Proceedings of the Conference (Vol. 1, pp. 102-115). Association for Computational Linguistics (ACL).
    • Le, P., & Titov, I. (2019). Boosting Entity Linking Performance by Leveraging Unlabeled Documents. 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. 1935-1945). The Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1187 [details]
    • Le, P., & Titov, I. (2019). Distant Learning for Entity Linking with Automatic Noise Detection. 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. 4081-4090). The Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1400 [details]
    • Liu, Y., Titov, I., & Lapata, M. (2019). Single Document Summarization as Tree Induction. 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. 1745-1755). The Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1173 [details]
    • Lyu, C., Cohen, S. B., & Titov, I. (2019). Semantic Role Labeling with Iterative Structure Refinement. In K. Inui, J. Jiang, V. Ng, & X. Wan (Eds.), 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing: EMNLP-IJCNLP 2019 : proceedings of the conference : November 3-7, 2019, Hong Kong, China (pp. 1071-1082). The Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1099 [details]
    • Voita, E., Sennrich, R., & Titov, I. (2019). Context-Aware Monolingual Repair for Neural Machine Translation. In K. Inui, J. Jiang, V. Ng, & X. Wan (Eds.), 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing: EMNLP-IJCNLP 2019 : proceedings of the conference : November 3-7, 2019, Hong Kong, China (pp. 877-886). The Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1081 [details]
    • Voita, E., Sennrich, R., & Titov, I. (2019). The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives. In K. Inui, J. Jiang, V. Ng, & X. Wan (Eds.), 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing: EMNLP-IJCNLP 2019 : proceedings of the conference : November 3-7, 2019, Hong Kong, China (pp. 4396-4406). The Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1448 [details]
    • Voita, E., Sennrich, R., & Titov, I. (2019). When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion. 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. 1198-1212). The Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1116 [details]
    • Voita, E., Talbot, D., Moiseev, F., Sennrich, R., & Titov, I. (2019). Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned. 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. 5797–5808). The Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1580 [details]
    • Wang, B., Titov, I., & Lapata, M. (2019). Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs. In K. Inui, J. Jiang, V. Ng, & X. Wan (Eds.), 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing: EMNLP-IJCNLP 2019 : proceedings of the conference : November 3-7, 2019, Hong Kong, China (pp. 3774-3785). The Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1391 [details]
    • Zhang, B., Titov, I., & Sennrich, R. (2019). Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention. In K. Inui, J. Jiang, V. Ng, & X. Wan (Eds.), 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing: EMNLP-IJCNLP 2019 : proceedings of the conference : November 3-7, 2019, Hong Kong, China (pp. 898-909). The Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1083 [details]

    2018

    • Bražinskas, A., Havrylov, S., & Titov, I. (2018). Embedding Words as Distributions with a Bayesian Skip-gram Model. In E. M. Bender, L. Derczynski, & P. Isabelle (Eds.), The 27th International Conference on Computational Linguistics: COLING 2018 : proceedings of the conference : August 20-26, 2018, Santa Fe, New Mexico, USA (pp. 1775-1789). Association for Computational Linguistics. https://www.aclweb.org/anthology/C18-1151/ [details]
    • Havrylov, S., & Titov, I. (2018). Emergence of language with multi-agent games: Learning to communicate with sequences of symbols. In U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, R. Fergus, S. V. N. Vishwanathan, & R. Garnett (Eds.), 31st Conference on Advances in Neural Information Processing Systems (NIPS 2017): Long Beach, California, USA, 4-9 December 2017 (pp. 2150-2160). (Advances in Neural Information Processing Systems; Vol. 30). Neural Information Processing Systems. https://papers.nips.cc/paper_files/paper/2017/hash/70222949cc0db89ab32c9969754d4758-Abstract.html
    • Le, P., & Titov, I. (2018). Improving entity linking by modeling latent relations between mentions. In ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) (Vol. 1, pp. 1595-1604). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p18-1148
    • Lyu, C., & Titov, I. (2018). AMR parsing as graph prediction with latent alignment. In ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) (Vol. 1, pp. 397-407). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p18-1037
    • Marcheggiani, D., Bastings, J., & Titov, I. (2018). Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks. 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. 2, pp. 486–492). The Association for Computational Linguistics. https://doi.org/10.18653/v1/N18-2078 [details]
    • Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018). Modeling Relational Data with Graph Convolutional Networks. In A. Gangemi, R. Navigli, M-E. Vidal, P. Hitzler, R. Troncy, L. Hollink, A. Tordai, & M. Alam (Eds.), The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018 : proceedings (pp. 593-607). (Lecture Notes in Computer Science; Vol. 10843). Springer. https://doi.org/10.1007/978-3-319-93417-4_38 [details]
    • Voita, E., Sennrich, R., Serdyukov, P., & Titov, I. (2018). Context-aware neural machine translation learns anaphora resolution. In ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) (Vol. 1, pp. 1264-1274). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p18-1117

    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.), Conference on Empirical Methods in Natural Language Processing: emnlp20017 : Copenhagen, Denmark, September 7-11, 2017 : conference proceedings: September 9-11, 2017, Copenhagen, Denmark (pp. 1957-1967). The Association for Computational Linguistics. https://doi.org/10.18653/v1/D17-1209 [details]
    • Le, P., & Titov, I. (2017). Optimizing differentiable relaxations of coreference evaluation metrics. In CoNLL 2017 - 21st Conference on Computational Natural Language Learning, Proceedings (pp. 390-399). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/k17-1039
    • Marcheggiani, D., & Titov, I. (2017). Encoding sentences with graph convolutional networks for semantic role labeling. In EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 1506-1515). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d17-1159
    • Marcheggiani, D., Frolov, A., & Titov, I. (2017). A simple and accurate syntax-agnostic neural model for dependency-based semantic role labeling. In CoNLL 2017 - 21st Conference on Computational Natural Language Learning, Proceedings (pp. 411-420). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/k17-1041

    2016

    • Šuster, S., Titov, I., & van Noord, G. (2016). Bilingual learning of multi-sense embeddings with discrete autoencoders. In K. Knight, A. Nenkova, & O. Rambow (Eds.), NAACL HLT 2016 : The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Proceedings of the Conference : June 12-17, 2016, San Diego, California, USA (pp. 1346-1356). The Association for Computational Linguistics. https://doi.org/10.18653/v1/N16-1160 [details]

    2015

    • Titov, I., & Khoddam, E. (2015). Unsupervised Induction of Semantic Roles within a Reconstruction-Error Minimization Framework. In R. Mihalcea, J. Chai, & A. Sarkar (Eds.), NAACL HLT 2015: The 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Proceedings of the Conference : May 31-June 5, 2015, Denver, Colorado, USA (pp. 1-10). The Association for Computational Linguistics. http://aclweb.org/anthology/N/N15/N15-1001.pdf [details]
    • Zhai, F., Szymanik, J., & Titov, I. (2015). Toward probabilistic natural logic for syllogistic reasoning. In T. Brochhagen, F. Roelofsen, & N. Theiler (Eds.), Proceedings of the 20th Amsterdam Colloquium (pp. 468-477). Institute for Logic, Language and Computation, University of Amsterdam. https://semanticsarchive.net/Archive/mVkOTk2N/AC2015-proceedings.pdf [details]

    2014

    • Frermann, L., Titov, I., & Pinkal, M. (2014). A Hierarchical Bayesian Model for Unsupervised Induction of Script Knowledge. In S. Wintner, S. Goldwater, & S. Riezler (Eds.), EACL 2014: 14th Conference of the European Chapter of the Association for Computational Linguistics: proceedings of the conference: April 26-30, 2014, Gothenburg, Sweden (pp. 49-57). Association for Computational Linguistics. http://www.aclweb.org/anthology/E/E14/E14-1006.pdf [details]
    • Kozhevnikov, M., & Titov, I. (2014). Cross-lingual Model Transfer Using Feature Representation Projection. In K. Toutanova, & H. Wu (Eds.), The 52nd Annual Meeting of the Association for Computational Linguistics: proceedings of the conference : ACL 2014 : June 22-27, Baltimore (Vol. 2, pp. 579-585). Association for Computational Linguistics. http://www.aclweb.org/anthology/P/P14/P14-2095.pdf [details]
    • Li, L., Titov, I., & Sporleder, C. (2014). Improved Estimation of Entropy for Evaluation of Word Sense Induction. Computational Linguistics, 40(3), 671-685. https://doi.org/10.1162/COLI_a_00196 [details]
    • Modi, A., & Titov, I. (2014). Inducing Neural Models of Script Knowledge. In R. Morante, & SW. Yih (Eds.), CoNNL-2014 : Eighteenth Conference on Computational Natural Language Learning: proceedings of the conference : June 26-27, 2014, Baltimore, Maryland, USA (pp. 49-57). The Association for Computational Linguistics. https://doi.org/10.3115/v1/W14-1606 [details]
    • Modi, A., & Titov, I. (2014). Learning Semantic Script Knowledge with Event Embeddings. In Workshop proceedings: papers accepted to the International Conference on Learning Representations (ICLR) 2014 ArXiv. https://arxiv.org/abs/1312.5198 [details]

    2013

    • Engonopoulos, N., Villalba, M., Titov, I., & Koller, A. (2013). Predicting the Resolution of Referring Expressions from User Behavior. In D. Yarowsky, T. Baldwin, A. Korhonen, K. Livescu, & S. Bethard (Eds.), EMNLP 2013 : 2013 Conference on Empirical Methods in Natural Language Processing: proceedings of the conference : 18-21 October 2013, Grand Hyatt Seattle, Seattle, Washington, USA (pp. 1354-1359). The Association for Computational Linguistics. http://aclweb.org/anthology/D/D13/D13-1134.pdf [details]
    • Henderson, J., Merlo, P., Titov, I., & Musillo, G. (2013). Multilingual Joint Parsing of Syntactic and Semantic Dependencies with a Latent Variable Model. Computational Linguistics, 39(4), 949-998. Advance online publication. https://doi.org/10.1162/COLI_a_00158 [details]
    • Kozhevnikov, M., & Titov, I. (2013). Bootstrapping Semantic Role Labelers from Parallel Data. In Second Joint Conference on Lexical and Computational Semantics : *SEM. - Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity : Atlanta, Georgia, June 13-14, 2013 (pp. 317-327). Association for Computational Linguistics. http://aclweb.org/anthology/S/S13/S13-1044.pdf [details]
    • Kozhevnikov, M., & Titov, I. (2013). Cross-lingual Transfer of Semantic Role Labeling Models. In P. Fung, & M. Poesio (Eds.), ACL 2013 : 51st Annual Meeting of the Association for Computational Linguistics: proceedings of the conference : August 4-9, 2013, Sofia, Bulgaria (Vol. 1, pp. 1190-1200). Association for Computational Linguistics. http://aclweb.org/anthology/P/P13/P13-1117.pdf [details]
    • Lazaridou, A., Titov, I., & Sporleder, C. (2013). A Bayesian Model for Joint Unsupervised Induction of Sentiment, Aspect and Discourse Representations. In P. Fung, & M. Poesio (Eds.), ACL 2013 : 51st Annual Meeting of the Association for Computational Linguistics: proceedings of the conference : August 4-9, 2013, Sofia, Bulgaria (Vol. 1, pp. 1630-1639). Association for Computational Linguistics. http://aclweb.org/anthology/P/P13/P13-1160.pdf [details]
    • Rohrbach, M., Qiu, W., Titov, I., Thater, S., Pinkal, M., & Schiele, B. (2013). Translating Video Content to Natural Language Descriptions. In 2013 IEEE International Conference on Computer Vision: ICCV 2013 : proceedings: 1-8 December 2013, Sydney, NSW, Australia (pp. 433-440). IEEE Computer Society. https://doi.org/10.1109/ICCV.2013.61 [details]

    2022

    • Wang, B., Titov, I., Andreas, J., & Kim, Y. (2022). Hierarchical Phrase-based Sequence-to-Sequence Learning. 8211-8229. Paper presented at 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates.

    2021

    • Schlichtkrull, M. S., De Cao, N., & Titov, I. (2021). Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking. Paper presented at 9th International Conference on Learning Representations, ICLR 2021, Virtual, Online. https://doi.org/10.48550/arXiv.2010.00577

    2017

    • Vaquero Patricio, C., Titov, I., & Honing, H. (2017). What score markings can say of the synergy between expressive timing and loudness. Abstract from European Society for Cognitive Sciences Of Music Conference, Ghent, Belgium.

    2016

    • Bražinskas, A., Havrylov, S., & Titov, I. A. (2016). Embedding Words as Distributions with a Bayesian Skip-gram Model. Paper presented at Bayesian Deep Learning Workshop NIPS 2016, Barcelona, Spain. http://bayesiandeeplearning.org/papers/BDL_25.pdf

    2024

    • De Cao, N. (2024). Entity centric neural models for natural language processing. [Thesis, fully internal, Universiteit van Amsterdam]. [details]

    2021

    • Schlichtkrull, M. S. (2021). Incorporating structure into neural models for language processing. [Thesis, fully internal, Universiteit van Amsterdam]. Institute for Logic, Language and Computation. [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]
    • Kipf, T. N. (2020). Deep learning with graph-structured representations. [Thesis, fully internal, Universiteit van Amsterdam]. [details]

    2019

    • Vaquero Patricio, C. (2019). What makes a perfomer unique? Idiosyncrasies and commonalities in expressive music performance. [Thesis, fully internal, Universiteit van Amsterdam]. Institute for Logic, Language and Computation. [details]

    2017

    • Hoàng, C. (2017). Latent domain models for statistical machine translation. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
    This list of publications is extracted from the UvA-Current Research Information System. Questions? Ask the library or the Pure staff of your faculty / institute. Log in to Pure to edit your publications. Log in to Personal Page Publication Selection tool to manage the visibility of your publications on this list.
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