| CrowS-pairs: A challenge dataset for measuring social biases in masked language models N Nangia, C Vania, R Bhalerao, SR Bowman arXiv preprint arXiv:2010.00133, 2020 | 665 | 2020 |
| Intermediate-task transfer learning with pretrained models for natural language understanding: When and why does it work? Y Pruksachatkun, J Phang, H Liu, PM Htut, X Zhang, RY Pang, C Vania, ... arXiv preprint arXiv:2005.00628, 2020 | 300 | 2020 |
| From Characters to Words to in Between: Do We Capture Morphology? C Vania, A Lopez arXiv preprint arXiv:1704.08352, 2017 | 115 | 2017 |
| English intermediate-task training improves zero-shot cross-lingual transfer too J Phang, I Calixto, PM Htut, Y Pruksachatkun, H Liu, C Vania, K Kann, ... arXiv preprint arXiv:2005.13013, 2020 | 75 | 2020 |
| A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages C Vania, Y Kementchedjhieva, A Søgaard, A Lopez arXiv preprint arXiv:1909.02857, 2019 | 65 | 2019 |
| Sentiment Lexicon Generation for an Under-Resourced Language C Vania, M Ibrahim, M Adriani Int. J. Comput. Linguistics Appl. 5 (1), 59-72, 2014 | 51 | 2014 |
| VisualSem: a high-quality knowledge graph for vision and language H Alberts, T Huang, Y Deshpande, Y Liu, K Cho, C Vania, I Calixto arXiv preprint arXiv:2008.09150, 2020 | 50 | 2020 |
| Automatically building a corpus for sentiment analysis on Indonesian tweets AF Wicaksono, C Vania, BT Distiawan, M Adriani Proceedings of the 28th Pacific Asia conference on language, information and …, 2014 | 50 | 2014 |
| Multilingual Probing Tasks for Word Representations G Gül Şahin, C Vania, I Kuznetsov, I Gurevych Computational Linguistics, 1-92, 2020 | 48* | 2020 |
| What do character-level models learn about morphology? The case of dependency parsing C Vania, A Grivas, A Lopez Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 41 | 2018 |
| Comparing test sets with item response theory C Vania, PM Htut, W Huang, D Mungra, RY Pang, J Phang, H Liu, K Cho, ... arXiv preprint arXiv:2106.00840, 2021 | 37 | 2021 |
| Indonli: A natural language inference dataset for indonesian R Mahendra, AF Aji, S Louvan, F Rahman, C Vania arXiv preprint arXiv:2110.14566, 2021 | 32 | 2021 |
| UniMorph 4.0: universal morphology K Batsuren, O Goldman, S Khalifa, N Habash, W Kieraś, G Bella, ... arXiv preprint arXiv:2205.03608, 2022 | 31 | 2022 |
| What ingredients make for an effective crowdsourcing protocol for difficult NLU data collection tasks? N Nangia, S Sugawara, H Trivedi, A Warstadt, C Vania, SR Bowman arXiv preprint arXiv:2106.00794, 2021 | 30 | 2021 |
| Sigmorphon 2021 shared task on morphological reinflection: Generalization across languages T Pimentel, M Ryskina, SJ Mielke, S Wu, E Chodroff, B Leonard, G Nicolai, ... SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and …, 2021 | 29 | 2021 |
| Endowing language models with multimodal knowledge graph representations N Huang, YR Deshpande, Y Liu, H Alberts, K Cho, C Vania, I Calixto arXiv preprint arXiv:2206.13163, 2022 | 23 | 2022 |
| University of Indonesia at TREC 2011 Microblog Task. S Louvan, M Ibrahim, M Adriani, C Vania, B Distiawan, MZ Wanagiri TREC, 2011 | 13 | 2011 |
| WebIE: Faithful and robust information extraction on the web C Whitehouse, C Vania, AF Aji, C Christodoulopoulos, A Pierleoni arXiv preprint arXiv:2305.14293, 2023 | 12 | 2023 |
| On understanding character-level models for representing morphology C Vania The University of Edinburgh, 2020 | 12 | 2020 |
| Asking crowdworkers to write entailment examples: The best of bad options C Vania, R Chen, SR Bowman arXiv preprint arXiv:2010.06122, 2020 | 11 | 2020 |
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