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ORCIDIvan Titov
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2020 – today
- 2025
[c115]Pedro Ferreira, Ivan Titov, Wilker Aziz:
Explanation Regularisation through the Lens of Attributions. COLING 2025: 6530-6551- 2024
- [b2]Ivan Titov:
Solovay Reducibility and Speedability Outside of left-c.e. Reals. Heidelberg University, Germany, 2024 - [c114]Masaru Isonuma, Ivan Titov:
Unlearning Traces the Influential Training Data of Language Models. ACL (1) 2024: 6312-6325 - [c113]Matthias Lindemann, Alexander Koller, Ivan Titov:
SIP: Injecting a Structural Inductive Bias into a Seq2Seq Model by Simulation. ACL (1) 2024: 6570-6587 - [c112]Guillem Ramírez, Matthias Lindemann, Alexandra Birch, Ivan Titov:
Cache & Distil: Optimising API Calls to Large Language Models. ACL (Findings) 2024: 11838-11853 - [c111]Verna Dankers, Ivan Titov:
Generalisation First, Memorisation Second? Memorisation Localisation for Natural Language Classification Tasks. ACL (Findings) 2024: 14348-14366 - [c110]Matthias Lindemann, Alexander Koller, Ivan Titov:
Strengthening Structural Inductive Biases by Pre-training to Perform Syntactic Transformations. EMNLP 2024: 11558-11573 - [c109]Victor Prokhorov, Ivan Titov, N. Siddharth:
Autoencoding Conditional Neural Processes for Representation Learning. ICML 2024 - [i91]Masaru Isonuma
, Ivan Titov:
Unlearning Reveals the Influential Training Data of Language Models. CoRR abs/2401.15241 (2024) - [i90]Guillem Ramírez, Alexandra Birch, Ivan Titov:
Optimising Calls to Large Language Models with Uncertainty-Based Two-Tier Selection. CoRR abs/2405.02134 (2024) - [i89]Matthias Lindemann, Alexander Koller, Ivan Titov:
Strengthening Structural Inductive Biases by Pre-training to Perform Syntactic Transformations. CoRR abs/2407.04543 (2024) - [i88]Ivan Titov:
Extending the Limit Theorem of Barmpalias and Lewis-Pye to all reals. CoRR abs/2407.14445 (2024) - [i87]Pedro Ferreira, Wilker Aziz, Ivan Titov:
Explanation Regularisation through the Lens of Attributions. CoRR abs/2407.16693 (2024) - [i86]Verna Dankers, Ivan Titov:
Generalisation First, Memorisation Second? Memorisation Localisation for Natural Language Classification Tasks. CoRR abs/2408.04965 (2024) - [i85]Zihan Qiu, Zeyu Huang, Shuang Cheng, Yizhi Zhou, Zili Wang, Ivan Titov, Jie Fu:
Layerwise Recurrent Router for Mixture-of-Experts. CoRR abs/2408.06793 (2024) - [i84]Zeyu Huang, Zihan Qiu, Zili Wang, Edoardo M. Ponti, Ivan Titov:
Post-hoc Reward Calibration: A Case Study on Length Bias. CoRR abs/2409.17407 (2024) - [i83]Ameen Ali, Lior Wolf, Ivan Titov:
Mitigating Copy Bias in In-Context Learning through Neuron Pruning. CoRR abs/2410.01288 (2024) - [i82]Hosein Mohebbi, Grzegorz Chrupala, Willem H. Zuidema, Afra Alishahi, Ivan Titov:
Disentangling Textual and Acoustic Features of Neural Speech Representations. CoRR abs/2410.03037 (2024) - [i81]Masaru Isonuma, Ivan Titov:
What's New in My Data? Novelty Exploration via Contrastive Generation. CoRR abs/2410.14765 (2024) - [i80]Ivan Titov:
Solovay reducibility via translation functions on rationals and on reals. CoRR abs/2410.15563 (2024) - [i79]John Gkountouras, Matthias Lindemann, Phillip Lippe, Efstratios Gavves, Ivan Titov:
Language Agents Meet Causality - Bridging LLMs and Causal World Models. CoRR abs/2410.19923 (2024) - 2023
- [c108]Matthias Lindemann, Alexander Koller, Ivan Titov:
Compositional Generalization without Trees using Multiset Tagging and Latent Permutations. ACL (1) 2023: 14488-14506 - [c107]Matthias Lindemann, Alexander Koller, Ivan Titov:
Compositional Generalisation with Structured Reordering and Fertility Layers. EACL 2023: 2164-2178 - [c106]Danis Alukaev, Semen Kiselev, Ilya Pershin, Bulat Ibragimov, Vladimir Ivanov, Alexey Kornaev, Ivan Titov:
Cross-Modal Conceptualization in Bottleneck Models. EMNLP 2023: 5241-5253 - [c105]Yanpeng Zhao, Ivan Titov:
On the Transferability of Visually Grounded PCFGs. EMNLP (Findings) 2023: 7895-7910 - [c104]Verna Dankers, Ivan Titov, Dieuwke Hupkes:
Memorisation Cartography: Mapping out the Memorisation-Generalisation Continuum in Neural Machine Translation. EMNLP 2023: 8323-8343 - [c103]Xinnuo Xu, Ivan Titov, Mirella Lapata:
Compositional Generalization for Data-to-Text Generation. EMNLP (Findings) 2023: 9299-9317 - [c102]Max Müller-Eberstein, Rob van der Goot, Barbara Plank, Ivan Titov:
Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training. EMNLP (Findings) 2023: 13190-13208 - [c101]Andrea Schioppa, Katja Filippova, Ivan Titov, Polina Zablotskaia:
Theoretical and Practical Perspectives on what Influence Functions Do. NeurIPS 2023 - [i78]Verna Dankers, Ivan Titov:
Recursive Neural Networks with Bottlenecks Diagnose (Non-)Compositionality. CoRR abs/2301.13714 (2023) - [i77]Matthias Lindemann, Alexander Koller, Ivan Titov:
Compositional Generalization without Trees using Multiset Tagging and Latent Permutations. CoRR abs/2305.16954 (2023) - [i76]Andrea Schioppa, Katja Filippova, Ivan Titov, Polina Zablotskaia:
Theoretical and Practical Perspectives on what Influence Functions Do. CoRR abs/2305.16971 (2023) - [i75]Victor Prokhorov, Ivan Titov, N. Siddharth:
Autoencoding Conditional Neural Processes for Representation Learning. CoRR abs/2305.18485 (2023) - [i74]Matthias Lindemann, Alexander Koller, Ivan Titov:
Injecting a Structural Inductive Bias into a Seq2Seq Model by Simulation. CoRR abs/2310.00796 (2023) - [i73]Guillem Ramírez, Matthias Lindemann, Alexandra Birch, Ivan Titov:
Cache & Distil: Optimising API Calls to Large Language Models. CoRR abs/2310.13561 (2023) - [i72]Yanpeng Zhao, Ivan Titov:
On the Transferability of Visually Grounded PCFGs. CoRR abs/2310.14107 (2023) - [i71]Danis Alukaev, Semen Kiselev, Ilya Pershin, Bulat Ibragimov, Vladimir Ivanov, Alexey Kornaev, Ivan Titov:
Cross-Modal Conceptualization in Bottleneck Models. CoRR abs/2310.14805 (2023) - [i70]Max Müller-Eberstein, Rob van der Goot, Barbara Plank, Ivan Titov:
Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training. CoRR abs/2310.16484 (2023) - [i69]Verna Dankers, Ivan Titov, Dieuwke Hupkes:
Memorisation Cartography: Mapping out the Memorisation-Generalisation Continuum in Neural Machine Translation. CoRR abs/2311.05379 (2023) - [i68]Maike Züfle, Verna Dankers, Ivan Titov:
Latent Feature-based Data Splits to Improve Generalisation Evaluation: A Hate Speech Detection Case Study. CoRR abs/2311.10236 (2023) - [i67]Xinnuo Xu, Ivan Titov, Mirella Lapata:
Compositional Generalization for Data-to-Text Generation. CoRR abs/2312.02748 (2023) - 2022
- [c100]Verna Dankers, Christopher G. Lucas, Ivan Titov:
Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation. ACL (1) 2022: 3608-3626 - [c99]Nicola De Cao, Leon Schmid, Dieuwke Hupkes, Ivan Titov:
Sparse Interventions in Language Models with Differentiable Masking. BlackboxNLP@EMNLP 2022: 16-27 - [c98]Verna Dankers, Ivan Titov:
Recursive Neural Networks with Bottlenecks Diagnose (Non-)Compositionality. EMNLP (Findings) 2022: 4361-4378 - [c97]Bailin Wang, Ivan Titov, Jacob Andreas, Yoon Kim:
Hierarchical Phrase-Based Sequence-to-Sequence Learning. EMNLP 2022: 8211-8229 - [i66]Verna Dankers, Christopher G. Lucas, Ivan Titov:
Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation. CoRR abs/2205.15301 (2022) - [i65]Matthias Lindemann, Alexander Koller, Ivan Titov:
Compositional Generalisation with Structured Reordering and Fertility Layers. CoRR abs/2210.03183 (2022) - [i64]Bailin Wang, Ivan Titov, Jacob Andreas, Yoon Kim:
Hierarchical Phrase-based Sequence-to-Sequence Learning. CoRR abs/2211.07906 (2022) - 2021
- [c96]Elena Voita, Rico Sennrich, Ivan Titov:
Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation. ACL/IJCNLP (1) 2021: 1126-1140 - [c95]Yuxuan Wang, Wanxiang Che, Ivan Titov, Shay B. Cohen, Zhilin Lei, Ting Liu:
A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples. ACL/IJCNLP (Findings) 2021: 2344-2354 - [c94]Biao Zhang, Ivan Titov, Barry Haddow, Rico Sennrich:
Beyond Sentence-Level End-to-End Speech Translation: Context Helps. ACL/IJCNLP (1) 2021: 2566-2578 - [c93]Biao Zhang, Ivan Titov, Rico Sennrich:
On Sparsifying Encoder Outputs in Sequence-to-Sequence Models. ACL/IJCNLP (Findings) 2021: 2888-2900 - [c92]Christos Baziotis, Ivan Titov, Alexandra Birch, Barry Haddow:
Exploring Unsupervised Pretraining Objectives for Machine Translation. ACL/IJCNLP (Findings) 2021: 2956-2971 - [c91]Henry Conklin, Bailin Wang, Kenny Smith, Ivan Titov:
Meta-Learning to Compositionally Generalize. ACL/IJCNLP (1) 2021: 3322-3335 - [c90]Nicola De Cao, Wilker Aziz, Ivan Titov:
Editing Factual Knowledge in Language Models. EMNLP (1) 2021: 6491-6506 - [c89]Biao Zhang, Ivan Titov, Rico Sennrich:
Sparse Attention with Linear Units. EMNLP (1) 2021: 6507-6520 - [c88]Nicola De Cao, Wilker Aziz, Ivan Titov:
Highly Parallel Autoregressive Entity Linking with Discriminative Correction. EMNLP (1) 2021: 7662-7669 - [c87]Elena Voita, Rico Sennrich, Ivan Titov:
Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT. EMNLP (1) 2021: 8478-8491 - [c86]Chunchuan Lyu, Shay B. Cohen, Ivan Titov:
A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing. EMNLP (1) 2021: 9075-9091 - [c85]Arthur Brazinskas, Mirella Lapata, Ivan Titov:
Learning Opinion Summarizers by Selecting Informative Reviews. EMNLP (1) 2021: 9424-9442 - [c84]Michael Sejr Schlichtkrull, Nicola De Cao, Ivan Titov:
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking. ICLR 2021 - [c83]Bailin Wang, Mirella Lapata, Ivan Titov:
Meta-Learning for Domain Generalization in Semantic Parsing. NAACL-HLT 2021: 366-379 - [c82]Bailin Wang, Mirella Lapata, Ivan Titov:
Learning from Executions for Semantic Parsing. NAACL-HLT 2021: 2747-2759 - [c81]Bailin Wang, Mirella Lapata, Ivan Titov:
Structured Reordering for Modeling Latent Alignments in Sequence Transduction. NeurIPS 2021: 13378-13391 - [i63]Yanpeng Zhao, Ivan Titov:
An Empirical Study of Compound PCFGs. CoRR abs/2103.02298 (2021) - [i62]Bailin Wang, Mirella Lapata, Ivan Titov:
Learning from Executions for Semantic Parsing. CoRR abs/2104.05819 (2021) - [i61]Biao Zhang, Ivan Titov, Rico Sennrich:
Sparse Attention with Linear Units. CoRR abs/2104.07012 (2021) - [i60]Nicola De Cao, Wilker Aziz, Ivan Titov:
Editing Factual Knowledge in Language Models. CoRR abs/2104.08164 (2021) - [i59]Bailin Wang, Mirella Lapata, Ivan Titov:
Structured Reordering for Modeling Latent Alignments in Sequence Transduction. CoRR abs/2106.03257 (2021) - [i58]Henry Conklin, Bailin Wang, Kenny Smith, Ivan Titov:
Meta-Learning to Compositionally Generalize. CoRR abs/2106.04252 (2021) - [i57]Christos Baziotis, Ivan Titov, Alexandra Birch, Barry Haddow:
Exploring Unsupervised Pretraining Objectives for Machine Translation. CoRR abs/2106.05634 (2021) - [i56]Elena Voita, Rico Sennrich, Ivan Titov:
Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT. CoRR abs/2109.01396 (2021) - [i55]Nicola De Cao, Wilker Aziz, Ivan Titov:
Highly Parallel Autoregressive Entity Linking with Discriminative Correction. CoRR abs/2109.03792 (2021) - [i54]Arthur Brazinskas, Mirella Lapata, Ivan Titov:
Learning Opinion Summarizers by Selecting Informative Reviews. CoRR abs/2109.04325 (2021) - [i53]Nicola De Cao, Leon Schmid, Dieuwke Hupkes, Ivan Titov:
Sparse Interventions in Language Models with Differentiable Masking. CoRR abs/2112.06837 (2021) - 2020
- [c80]Biao Zhang, Philip Williams, Ivan Titov, Rico Sennrich:
Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation. ACL 2020: 1628-1639 - [c79]Arthur Brazinskas, Mirella Lapata, Ivan Titov:
Unsupervised Opinion Summarization as Copycat-Review Generation. ACL 2020: 5151-5169 - [c78]Wolfgang Merkle, Ivan Titov:
Speedable Left-c.e. Numbers. CSR 2020: 303-313 - [c77]Elena Voita, Ivan Titov:
Information-Theoretic Probing with Minimum Description Length. EMNLP (1) 2020: 183-196 - [c76]Biao Zhang, Ivan Titov, Barry Haddow, Rico Sennrich:
Adaptive Feature Selection for End-to-End Speech Translation. EMNLP (Findings) 2020: 2533-2544 - [c75]Nicola De Cao, Michael Sejr Schlichtkrull, Wilker Aziz, Ivan Titov:
How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking. EMNLP (1) 2020: 3243-3255 - [c74]Diego Marcheggiani, Ivan Titov:
Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling. EMNLP (1) 2020: 3915-3928 - [c73]Arthur Brazinskas, Mirella Lapata, Ivan Titov:
Few-Shot Learning for Opinion Summarization. EMNLP (1) 2020: 4119-4135 - [c72]Yanpeng Zhao, Ivan Titov:
Visually Grounded Compound PCFGs. EMNLP (1) 2020: 4369-4379 - [c71]Denis Emelin, Ivan Titov, Rico Sennrich:
Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks. EMNLP (1) 2020: 7635-7653 - [c70]Zhifeng Hu, Serhii Havrylov, Ivan Titov, Shay B. Cohen:
Obfuscation for Privacy-preserving Syntactic Parsing. IWPT 2020 2020: 62-72 - [c69]Biao Zhang, Ivan Titov, Rico Sennrich:
Fast Interleaved Bidirectional Sequence Generation. WMT@EMNLP 2020: 503-515 - [i52]Elena Voita, Ivan Titov:
Information-Theoretic Probing with Minimum Description Length. CoRR abs/2003.12298 (2020) - [i51]Biao Zhang, Ivan Titov, Rico Sennrich:
On Sparsifying Encoder Outputs in Sequence-to-Sequence Models. CoRR abs/2004.11854 (2020) - [i50]Biao Zhang, Philip Williams, Ivan Titov, Rico Sennrich:
Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation. CoRR abs/2004.11867 (2020) - [i49]Serhii Havrylov, Ivan Titov:
Preventing Posterior Collapse with Levenshtein Variational Autoencoder. CoRR abs/2004.14758 (2020) - [i48]Arthur Brazinskas, Mirella Lapata, Ivan Titov:
Few-Shot Learning for Abstractive Multi-Document Opinion Summarization. CoRR abs/2004.14884 (2020) - [i47]Nicola De Cao, Michael Sejr Schlichtkrull, Wilker Aziz, Ivan Titov:
How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking. CoRR abs/2004.14992 (2020) - [i46]Yanpeng Zhao, Ivan Titov:
Unsupervised Transfer of Semantic Role Models from Verbal to Nominal Domain. CoRR abs/2005.00278 (2020) - [i45]Yanpeng Zhao, Ivan Titov:
Visually Grounded Compound PCFGs. CoRR abs/2009.12404 (2020) - [i44]Michael Sejr Schlichtkrull, Nicola De Cao, Ivan Titov:
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking. CoRR abs/2010.00577 (2020) - [i43]Biao Zhang, Ivan Titov, Barry Haddow, Rico Sennrich:
Adaptive Feature Selection for End-to-End Speech Translation. CoRR abs/2010.08518 (2020) - [i42]Elena Voita, Rico Sennrich, Ivan Titov:
Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation. CoRR abs/2010.10907 (2020) - [i41]Bailin Wang, Mirella Lapata, Ivan Titov:
Meta-Learning for Domain Generalization in Semantic Parsing. CoRR abs/2010.11988 (2020) - [i40]Chunchuan Lyu, Shay B. Cohen, Ivan Titov:
A Differentiable Relaxation of Graph Segmentation and Alignment for AMR Parsing. CoRR abs/2010.12676 (2020) - [i39]Biao Zhang, Ivan Titov, Rico Sennrich:
Fast Interleaved Bidirectional Sequence Generation. CoRR abs/2010.14481 (2020) - [i38]Denis Emelin, Ivan Titov, Rico Sennrich:
Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks. CoRR abs/2011.01846 (2020)
2010 – 2019
- 2019
- [c68]Elena Voita, Rico Sennrich, Ivan Titov:
When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion. ACL (1) 2019: 1198-1212 - [c67]Phong Le, Ivan Titov:
Boosting Entity Linking Performance by Leveraging Unlabeled Documents. ACL (1) 2019: 1935-1945 - [c66]Jasmijn Bastings, Wilker Aziz, Ivan Titov:
Interpretable Neural Predictions with Differentiable Binary Variables. ACL (1) 2019: 2963-2977 - [c65]Phong Le, Ivan Titov:
Distant Learning for Entity Linking with Automatic Noise Detection. ACL (1) 2019: 4081-4090 - [c64]Caio Corro, Ivan Titov:
Learning Latent Trees with Stochastic Perturbations and Differentiable Dynamic Programming. ACL (1) 2019: 5508-5521 - [c63]Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, Ivan Titov:
Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned. ACL (1) 2019: 5797-5808 - [c62]Elena Voita, Rico Sennrich, Ivan Titov:
Context-Aware Monolingual Repair for Neural Machine Translation. EMNLP/IJCNLP (1) 2019: 877-886 - [c61]Biao Zhang, Ivan Titov, Rico Sennrich:
Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention. EMNLP/IJCNLP (1) 2019: 898-909 - [c60]Chunchuan Lyu, Shay B. Cohen, Ivan Titov:
Semantic Role Labeling with Iterative Structure Refinement. EMNLP/IJCNLP (1) 2019: 1071-1082 - [c59]Bailin Wang, Ivan Titov, Mirella Lapata:
Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs. EMNLP/IJCNLP (1) 2019: 3772-3783 - [c58]Elena Voita, Rico Sennrich, Ivan Titov:
The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives. EMNLP/IJCNLP (1) 2019: 4395-4405 - [c57]Xinchi Chen, Chunchuan Lyu, Ivan Titov:
Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks. EMNLP/IJCNLP (1) 2019: 5414-5424 - [c56]Caio Corro, Ivan Titov:
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder. ICLR (Poster) 2019 - [c55]Alexandra Birch, Barry Haddow, Ivan Titov, Antonio Valerio Miceli Barone, Rachel Bawden, Felipe Sánchez-Martínez, Mikel L. Forcada, Miquel Esplà-Gomis, Víctor M. Sánchez-Cartagena, Juan Antonio Pérez-Ortiz, Wilker Aziz, Andrew Secker, Peggy van der Kreeft:
Global Under-Resourced Media Translation (GoURMET). MTSummit (2) 2019: 122 - [c54]Yang Liu, Ivan Titov, Mirella Lapata:
Single Document Summarization as Tree Induction. NAACL-HLT (1) 2019: 1745-1755 - [c53]Nicola De Cao, Wilker Aziz, Ivan Titov:
Question Answering by Reasoning Across Documents with Graph Convolutional Networks. NAACL-HLT (1) 2019: 2306-2317 - [c52]Nicola De Cao, Wilker Aziz, Ivan Titov:
Block Neural Autoregressive Flow. UAI 2019: 1263-1273 - [c51]Denis Emelin, Ivan Titov, Rico Sennrich:
Widening the Representation Bottleneck in Neural Machine Translation with Lexical Shortcuts. WMT (1) 2019: 102-115 - [i37]Jasmijn Bastings, Wilker Aziz, Ivan Titov, Khalil Sima'an:
Modeling Latent Sentence Structure in Neural Machine Translation. CoRR abs/1901.06436 (2019) - [i36]Nicola De Cao, Ivan Titov, Wilker Aziz:
Block Neural Autoregressive Flow. CoRR abs/1904.04676 (2019) - [i35]Zhifeng Hu, Serhii Havrylov, Ivan Titov, Shay B. Cohen:
Obfuscation for Privacy-preserving Syntactic Parsing. CoRR abs/1904.09585 (2019) - [i34]Elena Voita, Rico Sennrich, Ivan Titov:
When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion. CoRR abs/1905.05979 (2019) - [i33]Phong Le, Ivan Titov:
Distant Learning for Entity Linking with Automatic Noise Detection. CoRR abs/1905.07189 (2019) - [i32]Jasmijn Bastings, Wilker Aziz, Ivan Titov:
Interpretable Neural Predictions with Differentiable Binary Variables. CoRR abs/1905.08160 (2019) - [i31]Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, Ivan Titov:
Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned. CoRR abs/1905.09418 (2019) - [i30]Phong Le, Ivan Titov:
Boosting Entity Linking Performance by Leveraging Unlabeled Documents. CoRR abs/1906.01250 (2019) - [i29]Caio Corro, Ivan Titov:
Learning Latent Trees with Stochastic Perturbations and Differentiable Dynamic Programming. CoRR abs/1906.09992 (2019) - [i28]Denis Emelin, Ivan Titov, Rico Sennrich:
Widening the Representation Bottleneck in Neural Machine Translation with Lexical Shortcuts. CoRR abs/1906.12284 (2019) - [i27]Biao Zhang, Ivan Titov, Rico Sennrich:
Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention. CoRR abs/1908.11365 (2019) - [i26]Elena Voita, Rico Sennrich, Ivan Titov:
The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives. CoRR abs/1909.01380 (2019) - [i25]Elena Voita, Rico Sennrich, Ivan Titov:
Context-Aware Monolingual Repair for Neural Machine Translation. CoRR abs/1909.01383 (2019) - [i24]Chunchuan Lyu, Shay B. Cohen, Ivan Titov:
Semantic Role Labeling with Iterative Structure Refinement. CoRR abs/1909.03285 (2019) - [i23]Bailin Wang, Ivan Titov, Mirella Lapata:
Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs. CoRR abs/1909.04165 (2019) - [i22]Diego Marcheggiani, Ivan Titov:
Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling. CoRR abs/1909.09814 (2019) - [i21]Xinchi Chen, Chunchuan Lyu, Ivan Titov:
Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks. CoRR abs/1910.03136 (2019) - [i20]Shangmin Guo, Yi Ren, Serhii Havrylov, Stella Frank, Ivan Titov, Kenny Smith:
The Emergence of Compositional Languages for Numeric Concepts Through Iterated Learning in Neural Agents. CoRR abs/1910.05291 (2019) - [i19]Arthur Brazinskas, Mirella Lapata, Ivan Titov:
Unsupervised Multi-Document Opinion Summarization as Copycat-Review Generation. CoRR abs/1911.02247 (2019) - 2018
- [c50]Chunchuan Lyu, Ivan Titov:
AMR Parsing as Graph Prediction with Latent Alignment. ACL (1) 2018: 397-407 - [c49]Elena Voita, Pavel Serdyukov, Rico Sennrich, Ivan Titov:
Context-Aware Neural Machine Translation Learns Anaphora Resolution. ACL (1) 2018: 1264-1274 - [c48]Phong Le, Ivan Titov:
Improving Entity Linking by Modeling Latent Relations between Mentions. ACL (1) 2018: 1595-1604 - [c47]Arthur Brazinskas, Serhii Havrylov, Ivan Titov:
Embedding Words as Distributions with a Bayesian Skip-gram Model. COLING 2018: 1775-1789 - [c46]Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling:
Modeling Relational Data with Graph Convolutional Networks. ESWC 2018: 593-607 - [c45]Diego Marcheggiani, Jasmijn Bastings, Ivan Titov:
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks. NAACL-HLT (2) 2018: 486-492 - [e1]Anna Korhonen, Ivan Titov:
Proceedings of the 22nd Conference on Computational Natural Language Learning, CoNLL 2018, Brussels, Belgium, October 31 - November 1, 2018. Association for Computational Linguistics 2018, ISBN 978-1-948087-72-8 [contents] - [i18]Diego Marcheggiani, Jasmijn Bastings, Ivan Titov:
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks. CoRR abs/1804.08313 (2018) - [i17]Phong Le, Ivan Titov:
Improving Entity Linking by Modeling Latent Relations between Mentions. CoRR abs/1804.10637 (2018) - [i16]Chunchuan Lyu, Ivan Titov:
AMR Parsing as Graph Prediction with Latent Alignment. CoRR abs/1805.05286 (2018) - [i15]Elena Voita, Pavel Serdyukov, Rico Sennrich, Ivan Titov:
Context-Aware Neural Machine Translation Learns Anaphora Resolution. CoRR abs/1805.10163 (2018) - [i14]Caio Corro, Ivan Titov:
Differentiable Perturb-and-Parse: Semi-Supervised Parsing with a Structured Variational Autoencoder. CoRR abs/1807.09875 (2018) - [i13]Nicola De Cao, Wilker Aziz, Ivan Titov:
Question Answering by Reasoning Across Documents with Graph Convolutional Networks. CoRR abs/1808.09920 (2018) - 2017
- [j6]Ashutosh Modi, Ivan Titov, Vera Demberg, Asad B. Sayeed, Manfred Pinkal:
Modelling Semantic Expectation: Using Script Knowledge for Referent Prediction. Trans. Assoc. Comput. Linguistics 5: 31-44 (2017) - [c44]Phong Le, Ivan Titov:
Optimizing Differentiable Relaxations of Coreference Evaluation Metrics. CoNLL 2017: 390-399 - [c43]Diego Marcheggiani, Anton Frolov, Ivan Titov:
A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling. CoNLL 2017: 411-420 - [c42]Diego Marcheggiani, Ivan Titov:
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling. EMNLP 2017: 1506-1515 - [c41]Jasmijn Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima'an:
Graph Convolutional Encoders for Syntax-aware Neural Machine Translation. EMNLP 2017: 1957-1967 - [c40]Serhii Havrylov, Ivan Titov:
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols. ICLR (Workshop) 2017 - [c39]Serhii Havrylov, Ivan Titov:
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols. NIPS 2017: 2149-2159 - [i12]Diego Marcheggiani, Anton Frolov, Ivan Titov:
A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling. CoRR abs/1701.02593 (2017) - [i11]Ashutosh Modi, Ivan Titov, Vera Demberg, Asad B. Sayeed, Manfred Pinkal:
Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction. CoRR abs/1702.03121 (2017) - [i10]Diego Marcheggiani, Ivan Titov:
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling. CoRR abs/1703.04826 (2017) - [i9]Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling:
Modeling Relational Data with Graph Convolutional Networks. CoRR abs/1703.06103 (2017) - [i8]Phong Le, Ivan Titov:
Optimizing Differentiable Relaxations of Coreference Evaluation Metrics. CoRR abs/1704.04451 (2017) - [i7]Jasmijn Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, Khalil Sima'an:
Graph Convolutional Encoders for Syntax-aware Neural Machine Translation. CoRR abs/1704.04675 (2017) - [i6]Serhii Havrylov, Ivan Titov:
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols. CoRR abs/1705.11192 (2017) - [i5]Arthur Brazinskas, Serhii Havrylov, Ivan Titov:
Embedding Words as Distributions with a Bayesian Skip-gram Model. CoRR abs/1711.11027 (2017) - 2016
- [j5]Cuong Hoang, Khalil Sima'an, Ivan Titov:
Adapting to All Domains at Once: Rewarding Domain Invariance in SMT. Trans. Assoc. Comput. Linguistics 4: 99-112 (2016) - [j4]Diego Marcheggiani, Ivan Titov:
Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations. Trans. Assoc. Comput. Linguistics 4: 231-244 (2016) - [c38]Simon Suster, Ivan Titov, Gertjan van Noord:
Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders. HLT-NAACL 2016: 1346-1356 - [i4]Simon Suster, Ivan Titov, Gertjan van Noord:
Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders. CoRR abs/1603.09128 (2016) - 2015
- [c37]Ivan Titov, Ehsan Khoddam:
Unsupervised Induction of Semantic Roles within a Reconstruction-Error Minimization Framework. HLT-NAACL 2015: 1-10 - [c36]Ivan Titov, Ehsan Khoddam:
Inducing Semantic Representation from Text by Jointly Predicting and Factorizing Relations. ICLR (Workshop) 2015 - [i3]Simon Suster, Gertjan van Noord, Ivan Titov:
Word Representations, Tree Models and Syntactic Functions. CoRR abs/1508.07709 (2015) - 2014
- [j3]Linlin Li, Ivan Titov, Caroline Sporleder:
Improved Estimation of Entropy for Evaluation of Word Sense Induction. Comput. Linguistics 40(3): 671-685 (2014) - [c35]Mikhail Kozhevnikov, Ivan Titov:
Cross-lingual Model Transfer Using Feature Representation Projection. ACL (2) 2014: 579-585 - [c34]Ashutosh Modi, Ivan Titov:
Inducing Neural Models of Script Knowledge. CoNLL 2014: 49-57 - [c33]Lea Frermann, Ivan Titov, Manfred Pinkal:
A Hierarchical Bayesian Model for Unsupervised Induction of Script Knowledge. EACL 2014: 49-57 - [c32]Ashutosh Modi, Ivan Titov:
Learning Semantic Script Knowledge with Event Embeddings. ICLR (Workshop) 2014 - [i2]Ivan Titov, Ehsan Khoddam:
Unsupervised Induction of Semantic Roles within a Reconstruction-Error Minimization Framework. CoRR abs/1412.2812 (2014) - 2013
- [j2]James Henderson, Paola Merlo, Ivan Titov, Gabriele Musillo:
Multilingual Joint Parsing of Syntactic and Semantic Dependencies with a Latent Variable Model. Comput. Linguistics 39(4): 949-998 (2013) - [c31]Mikhail Kozhevnikov, Ivan Titov:
Cross-lingual Transfer of Semantic Role Labeling Models. ACL (1) 2013: 1190-1200 - [c30]Angeliki Lazaridou, Ivan Titov, Caroline Sporleder:
A Bayesian Model for Joint Unsupervised Induction of Sentiment, Aspect and Discourse Representations. ACL (1) 2013: 1630-1639 - [c29]Nikos Engonopoulos, Martin Villalba, Ivan Titov, Alexander Koller:
Predicting the Resolution of Referring Expressions from User Behavior. EMNLP 2013: 1354-1359 - [c28]Marcus Rohrbach, Wei Qiu, Ivan Titov, Stefan Thater, Manfred Pinkal, Bernt Schiele:
Translating Video Content to Natural Language Descriptions. ICCV 2013: 433-440 - [c27]Martha Palmer, Ivan Titov, Shumin Wu:
Semantic Role Labeling. HLT-NAACL 2013: 10-12 - [c26]Mikhail Kozhevnikov, Ivan Titov:
Bootstrapping Semantic Role Labelers from Parallel Data. *SEM@NAACL-HLT 2013: 317-327 - 2012
- [c25]Ivan Titov, Alexandre Klementiev:
Crosslingual Induction of Semantic Roles. ACL (1) 2012: 647-656 - [c24]Alexandre Klementiev, Ivan Titov, Binod Bhattarai:
Inducing Crosslingual Distributed Representations of Words. COLING 2012: 1459-1474 - [c23]Ivan Titov, Alexandre Klementiev:
Semi-Supervised Semantic Role Labeling: Approaching from an Unsupervised Perspective. COLING 2012: 2635-2652 - [c22]Ivan Titov, Alexandre Klementiev:
A Bayesian Approach to Unsupervised Semantic Role Induction. EACL 2012: 12-22 - 2011
- [c21]Ivan Titov:
Domain Adaptation by Constraining Inter-Domain Variability of Latent Feature Representation. ACL 2011: 62-71 - [c20]Ivan Titov, Alexandre Klementiev:
A Bayesian Model for Unsupervised Semantic Parsing. ACL 2011: 1445-1455 - 2010
- [j1]James Henderson, Ivan Titov:
Incremental Sigmoid Belief Networks for Grammar Learning. J. Mach. Learn. Res. 11: 3541-3570 (2010) - [c19]Minwoo Jeong, Ivan Titov:
Unsupervised Discourse Segmentation of Documents with Inherently Parallel Structure. ACL (2) 2010: 151-155 - [c18]Ivan Titov, Mikhail Kozhevnikov:
Bootstrapping Semantic Analyzers from Non-Contradictory Texts. ACL 2010: 958-967 - [c17]Minwoo Jeong, Ivan Titov:
Multi-document topic segmentation. CIKM 2010: 1119-1128 - [c16]Ivan Titov, Alexandre Klementiev, Kevin Small, Dan Roth:
Unsupervised Aggregation for Classification Problems with Large Numbers of Categories. AISTATS 2010: 836-843 - [p1]Ivan Titov, James Henderson:
A Latent Variable Model for Generative Dependency Parsing. Trends in Parsing Technology 2010: 35-55
2000 – 2009
- 2009
- [c15]Andrea Gesmundo, James Henderson, Paola Merlo, Ivan Titov:
A Latent Variable Model of Synchronous Syntactic-Semantic Parsing for Multiple Languages. CoNLL Shared Task 2009: 37-42 - [c14]Alexandre Klementiev, Dan Roth, Kevin Small, Ivan Titov:
Unsupervised Rank Aggregation with Domain-Specific Expertise. IJCAI 2009: 1101-1106 - [c13]Ivan Titov, James Henderson, Paola Merlo, Gabriele Musillo:
Online Graph Planarisation for Synchronous Parsing of Semantic and Syntactic Dependencies. IJCAI 2009: 1562-1567 - [c12]Dan Roth, Kevin Small, Ivan Titov:
Sequential Learning of Classifiers for Structured Prediction Problems. AISTATS 2009: 440-447 - 2008
- [b1]Ivan Titov:
Exploiting non-linear probabilistic models in natural language parsing and reranking. University of Geneva, Switzerland, 2008 - [c11]Ivan Titov, Ryan T. McDonald:
A Joint Model of Text and Aspect Ratings for Sentiment Summarization. ACL 2008: 308-316 - [c10]James Henderson, Paola Merlo, Gabriele Musillo, Ivan Titov:
A Latent Variable Model of Synchronous Parsing for Syntactic and Semantic Dependencies. CoNLL 2008: 178-182 - [c9]Ivan Titov, Ryan T. McDonald:
Modeling online reviews with multi-grain topic models. WWW 2008: 111-120 - [i1]Ivan Titov, Ryan T. McDonald:
Modeling Online Reviews with Multi-grain Topic Models. CoRR abs/0801.1063 (2008) - 2007
- [c8]Ivan Titov, James Henderson:
Constituent Parsing with Incremental Sigmoid Belief Networks. ACL 2007 - [c7]Ivan Titov, James Henderson:
Fast and Robust Multilingual Dependency Parsing with a Generative Latent Variable Model. EMNLP-CoNLL 2007: 947-951 - [c6]Ivan Titov, James Henderson:
Incremental Bayesian networks for structure prediction. ICML 2007: 887-894 - [c5]Ivan Titov, James Henderson:
A Latent Variable Model for Generative Dependency Parsing. IWPT 2007: 144-155 - 2006
- [c4]Ivan Titov, James Henderson:
Porting Statistical Parsers with Data-Defined Kernels. CoNLL 2006: 6-13 - [c3]Ivan Titov, James Henderson:
Loss Minimization in Parse Reranking. EMNLP 2006: 560-567 - 2005
- [c2]James Henderson, Ivan Titov:
Data-Defined Kernels for Parse Reranking Derived from Probabilistic Models. ACL 2005: 181-188 - [c1]Ivan Titov, James Henderson:
Deriving kernels from MLP probability estimators for large categorization problems. IJCNN 2005: 937-942
Coauthor Index
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