| Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned E Voita, D Talbot, F Moiseev, R Sennrich, I Titov ACL 2019, 2019 | 1371 | 2019 |
| Context-Aware Neural Machine Translation Learns Anaphora Resolution E Voita, P Serdyukov, R Sennrich, I Titov ACL 2018, 2018 | 345 | 2018 |
| BPE-Dropout: Simple and Effective Subword Regularization I Provilkov, D Emelianenko, E Voita ACL 2020, 2019 | 313 | 2019 |
| Information-Theoretic Probing with Minimum Description Length E Voita, I Titov EMNLP 2020, 2020 | 277 | 2020 |
| When a Good Translation is Wrong in Context: Context-Aware Machine Translation Improves on Deixis, Ellipsis, and Lexical Cohesion E Voita, R Sennrich, I Titov ACL 2019, 2019 | 255 | 2019 |
| The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives E Voita, R Sennrich, I Titov EMNLP 2019, 2019 | 187 | 2019 |
| A Large-Scale Test Set for the Evaluation of Context-Aware Pronoun Translation in Neural Machine Translation M Müller, A Rios, E Voita, R Sennrich WMT 2018, 61-72, 2018 | 159 | 2018 |
| Context-Aware Monolingual Repair for Neural Machine Translation E Voita, R Sennrich, I Titov EMNLP 2019, 2019 | 112 | 2019 |
| Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translation NM Guerreiro, E Voita, AFT Martins EACL 2023, 2023 | 101 | 2023 |
| Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation E Voita, R Sennrich, I Titov ACL 2021, 2020 | 86 | 2020 |
| Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better D Dale, E Voita, L Barrault, MR Costa-jussà ACL 2023, 2023 | 56 | 2023 |
| Neurons in Large Language Models: Dead, N-Gram, Positional E Voita, J Ferrando, C Nalmpantis ACL 2024 (findings), 2023 | 42 | 2023 |
| HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translation D Dale, E Voita, J Lam, P Hansanti, C Ropers, E Kalbassi, C Gao, ... EMNLP 2023, 2023 | 28 | 2023 |
| Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT E Voita, R Sennrich, I Titov EMNLP 2021, 2021 | 27 | 2021 |
| Sequence Modeling with Unconstrained Generation Order D Emelianenko, E Voita, P Serdyukov NeurIPS 2019, 2019 | 23 | 2019 |
| Information Flow Routes: Automatically Interpreting Language Models at Scale J Ferrando, E Voita EMNLP 2024, 2024 | 17 | 2024 |
| Embedding Words in Non-Vector Space with Unsupervised Graph Learning M Ryabinin, S Popov, L Prokhorenkova, E Voita EMNLP 2020, 2020 | 10 | 2020 |
| LM Transparency Tool: Interactive Tool for Analyzing Transformer Language Models I Tufanov, K Hambardzumyan, J Ferrando, E Voita ACL 2024 demo, 2024 | 7 | 2024 |
| Know When To Stop: A Study of Semantic Drift in Text Generation A Spataru, E Hambro, E Voita, N Cancedda NAACL 2024, 2024 | 3 | 2024 |
| Proceedings of the First edition of the Workshop on the Scaling Behavior of Large Language Models (SCALE-LLM 2024) AV Miceli-Barone, F Barez, SB Cohen, E Voita, U Germann, M Lukasik Proceedings of the First edition of the Workshop on the Scaling Behavior of …, 2024 | | 2024 |
Ivan TitovUniversity of Edinburgh / University of AmsterdamEmail verificata su inf.ed.ac.uk