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]
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). Stroudsburg, PA: 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
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). Stroudsburg, PA: 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). Stroudsburg, PA: 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). Stroudsburg, PA: 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). Stroudsburg, PA: 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). Stroudsburg, PA: 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). Stroudsburg, PA: 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). Stroudsburg, PA: 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). Stroudsburg, PA: 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). Stroudsburg, PA: The Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1083[details]
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]
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). Stroudsburg, PA: 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]
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]
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). Stroudsburg, PA: The Association for Computational Linguistics. https://doi.org/10.18653/v1/N16-1160[details]
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). Stroudsburg, PA: The Association for Computational Linguistics. [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]
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). Stroudsburg, PA: Association for Computational Linguistics. [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). Stroudsburg, PA: Association for Computational Linguistics. [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). Stroudsburg, PA: 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 Ithaca, NY: arXiv.org. [details]
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). Stroudsburg, PA: The Association for Computational Linguistics. [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. 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). Sroudsburg, PA: Association for Computational Linguistics. [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). Stroudsburg, PA: Association for Computational Linguistics. [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). Stroudsburg, PA: Association for Computational Linguistics. [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). Los Alamitos, California: IEEE Computer Society. https://doi.org/10.1109/ICCV.2013.61[details]
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.
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.
2021
Schlichtkrull, M. S. (2021). Incorporating structure into neural models for language processing. Institute for Logic, Language and Computation. [details]
Bastings, J. (2020). A tale of two sequences: Interpretable and linguistically-informed deep learning for natural language processing. Institute for Logic, Language and Computation. [details]
Vaquero Patricio, C. (2019). What makes a perfomer unique? Idiosyncrasies and commonalities in expressive music performance. Amsterdam: Institute for Logic, Language and Computation. [details]
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