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Dr. I.A. (Ivan) Titov

Faculteit der Natuurwetenschappen, Wiskunde en Informatica
ILLC
Bezoekadres
  • Science Park 900
  • Kamernummer: L6.52
Postadres
  • Postbus 94242
    1090 GE Amsterdam
Contactgegevens
  • Publicaties

    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

    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 M.-C. Moens, X. Huang, L. Specia, & S. W. Yih (Eds.), 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 [details]
    • Lyu, C., Cohen, S. B., & Titov, I. (2021). A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing. In M.-C. Moens, X. Huang, L. Specia, & S. W. Yih (Eds.), 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 [details]
    • 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 [details]
    • Voita, E., Sennrich, R., & Titov, I. (2021). Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT. In M.-C. Moens, X. Huang, L. Specia, & S. W. Yih (Eds.), 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 [details]
    • 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 [details]
    • 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 [details]
    • 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 [details]
    • 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 [details]
    • 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 [details]
    • 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 [details]
    • 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.), The Second Workshop on Domain Adaptation for NLP: Adap-NLP 2021 : Proceedings of the Workshop : April 20, 2021 (pp. 166-171). Association for Computational Linguistics. https://aclanthology.org/2021.adaptnlp-1.17 [details]

    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 G. Bouma, Y. Matsumoto, S. Oepen, K. Sagae, D. Seddah, W. Sun, A. Søgaard, R. Tsarfaty, & D. Zeman (Eds.), The 16th International Conference on Parsing Technologies and IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies: IWPT 2020 : Proceedings of the Conference : July 9, 2020 (pp. 62-72). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.iwpt-1.7 [details]
    • Marcheggiani, D., & Titov, I. (2020). Graph convolutions over constituent trees for syntax-aware semantic role labeling. 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. 3915-3928). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.322 [details]
    • Voita, E., & Titov, I. (2020). Information-theoretic probing with minimum description length. 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. 183-196). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.14 [details]
    • Zhang, B., Titov, I., & Sennrich, R. (2020). Fast Interleaved Bidirectional Sequence Generation. In L. Barrault, O. Bojar, F. Bougares, R. Chatterjee, M. R. Costa-Jussà, C. Federmann, M. Fishel, A. Fraser, Y. Graham, P. Guzman, B. Haddow, M. Huck, A. Jimeno Yepes, P. Koehn, A. Martins, M. Morishita, C. Monz, M. Nagata, T. Nakazawa, & M. Negri (Eds.), Fifth Conference on Machine Translation : proceedings of the conference: EMNLP : November 19-20, 2020, online (pp. 503-518). Association for Computational Linguistics. https://aclanthology.org/2020.wmt-1.62 [details]
    • 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 [details]
    • Zhang, B., Williams, P., Titov, I., & Sennrich, R. (2020). Improving massively multilingual neural machine translation and zero-shot translation. 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. 1628-1639). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.148 [details]
    • Zhao, Y., & Titov, I. (2020). Visually grounded compound PCFGs. 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. 4369-4379). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.354 [details]

    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 O. Bojar, R. Chatterjee, C. Federmann, M. Fishel, Y. Graham, B. Haddow, M. Huck, A. Jimeno Yepes, P. Koehn, A. Martins, C. Monz, M. Negri, A. Névéol, M. Neves, M. Post, M. Turchi, & K. Verspoor (Eds.), Fourth Conference on Machine Translation - Proceedings of the Conference: WMT 2019 (Vol. 1, pp. 102-115). Association for Computational Linguistics. https://doi.org/10.18653/v1/W19-5211 [details]
    • 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 (Vol. 4, 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 [details]
    • Le, P., & Titov, I. (2018). Improving entity linking by modeling latent relations between mentions. In I. Gurevych, & Y. Miyao (Eds.), ACL 2018 : The 56th Annual Meeting of the Association for Computational Linguistics: proceedings of the conference : July 15-20, 2018, Melbourne, Australia (Vol. 1, pp. 1595-1604). The Association for Computational Linguistics. https://doi.org/10.18653/v1/p18-1148 [details]
    • Lyu, C., & Titov, I. (2018). AMR parsing as graph prediction with latent alignment. In I. Gurevych, & Y. Miyao (Eds.), ACL 2018 : The 56th Annual Meeting of the Association for Computational Linguistics: proceedings of the conference : July 15-20, 2018, Melbourne, Australia (Vol. 1, pp. 397-407). The Association for Computational Linguistics. https://doi.org/10.18653/v1/p18-1037 [details]
    • 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., Serdyukov, P., Sennrich, R., & Titov, I. (2018). Context-aware neural machine translation learns anaphora resolution. In I. Gurevych, & Y. Miyao (Eds.), ACL 2018 : The 56th Annual Meeting of the Association for Computational Linguistics: proceedings of the conference : July 15-20, 2018, Melbourne, Australia (Vol. 1, pp. 1264-1274). The Association for Computational Linguistics. https://doi.org/10.18653/v1/p18-1117 [details]

    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.), The Conference on Empirical Methods in Natural Language Processing: proceedings of the conference : EMNLP 2017 : September 9-11, 2017, Copenhagen, Denmark (pp. 1957-1967). 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 R. Levy, & L. Specia (Eds.), The 21st Conference on Computational Natural Language Learning: Proceedings of the Conference : CoNNL 2017 : August 2-august 4, 2017, Vancouver, Canada (pp. 390-399). Association for Computational Linguistics. https://doi.org/10.18653/v1/k17-1039 [details]
    • Marcheggiani, D., & Titov, I. (2017). Encoding sentences with graph convolutional networks for semantic role labeling. In M. Palmer, R. Hwa, & S. Riedel (Eds.), The Conference on Empirical Methods in Natural Language Processing: proceedings of the conference : EMNLP 2017 : September 9-11, 2017, Copenhagen, Denmark (pp. 1506-1515). Association for Computational Linguistics. https://doi.org/10.18653/v1/d17-1159 [details]
    • Marcheggiani, D., Frolov, A., & Titov, I. (2017). A simple and accurate syntax-agnostic neural model for dependency-based semantic role labeling. In R. Levy, & L. Specia (Eds.), The 21st Conference on Computational Natural Language Learning: Proceedings of the Conference : CoNNL 2017 : August 2-august 4, 2017, Vancouver, Canada (pp. 411-420). Association for Computational Linguistics. https://doi.org/10.18653/v1/k17-1041 [details]

    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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