| From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers A Lauscher, V Ravishankar, I Vulić, G Glavaš EMNLP 2020, 4483-4499, 2020 | 350* | 2020 |
| Takelab: Systems for measuring semantic text similarity F Šarić, G Glavaš, M Karan, J Šnajder, BD Bašić SemEval 2012, 441-448, 2012 | 293 | 2012 |
| Probing pretrained language models for lexical semantics I Vulić, EM Ponti, R Litschko, G Glavaš, A Korhonen EMNLP 2020, 7222-7240, 2020 | 258 | 2020 |
| XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning EM Ponti, G Glavaš, O Majewska, Q Liu, I Vulić, A Korhonen EMNLP 2020, 2362-2376, 2020 | 239 | 2020 |
| Simplifying lexical simplification: Do we need simplified corpora? G Glavaš, S Štajner ACL 2015, 63-68, 2015 | 213 | 2015 |
| How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions G Glavaš, R Litschko, S Ruder, I Vulić ACL 2019, 710-721, 2019 | 207 | 2019 |
| RedditBias: A real-world resource for bias evaluation and debiasing of conversational language models S Barikeri, A Lauscher, I Vulić, G Glavaš ACL 2021, 2021 | 182 | 2021 |
| Sustainable modular debiasing of language models A Lauscher, T Lueken, G Glavaš Findings of the ACL: EMNLP 2021, 2021 | 144 | 2021 |
| Unsupervised text segmentation using semantic relatedness graphs G Glavaš, F Nanni, SP Ponzetto *SEM 2016, 125-130, 2016 | 144 | 2016 |
| Specializing unsupervised pretraining models for word-level semantic similarity A Lauscher, I Vulić, EM Ponti, A Korhonen, G Glavaš COLING 2020, 1371-1383, 2020 | 119* | 2020 |
| Event graphs for information retrieval and multi-document summarization G Glavaš, J Šnajder Expert systems with applications 41 (15), 6904-6916, 2014 | 117 | 2014 |
| Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? I Vulić, G Glavaš, R Reichart, A Korhonen EMNLP 2019, 4406-4417, 2019 | 105 | 2019 |
| Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers A Lauscher, O Majewska, LFR Ribeiro, I Gurevych, N Rozanov, G Glavaš Deep Learning Inside Out Workshop (DeeLIO), 43-49, 2020 | 96 | 2020 |
| Explicit Retrofitting of Distributional Word Vectors G Glavaš, I Vulić ACL 2018, 34-45, 2018 | 90 | 2018 |
| MAD-G: Multilingual adapter generation for efficient cross-lingual transfer A Ansell, EM Ponti, J Pfeiffer, S Ruder, G Glavaš, I Vulić, A Korhonen Findings of the ACL: EMNLP 2021, 4762-4781, 2021 | 87 | 2021 |
| Unsupervised cross-lingual information retrieval using monolingual data only R Litschko, G Glavaš, SP Ponzetto, I Vulić SIGIR 2018, 1253-1256, 2018 | 86 | 2018 |
| XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages G Glavaš, M Karan, I Vulić COLING 2020, 6350-6365, 2020 | 77 | 2020 |
| Is Supervised Syntactic Parsing Beneficial for Language Understanding Tasks? An Empirical Investigation G Glavaš, I Vulić EACL 2021, 3090-3104, 2021 | 76* | 2021 |
| A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces. A Lauscher, G Glavas, SP Ponzetto, I Vulic AAAI 2020, 8131-8138, 2020 | 75 | 2020 |
| An argument-annotated corpus of scientific publications A Lauscher, G Glavaš, SP Ponzetto Association for Computational Linguistics, 2018 | 71 | 2018 |
Ivan VulićGoogle DeepMind & University of Cambridge確認したメール アドレス: cam.ac.uk