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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 1 – 20 of 22 1 IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages ... Bugliarello, Emanuele; Liu, Fangyu; Pfeiffer, Jonas. - : arXiv, 2022 BASE Show details 2 MDAPT: Multilingual Domain Adaptive Pretraining in a Single Model ... Jørgensen, Rasmus Kær; Hartmann, Mareike; Dai, Xiang. - : arXiv, 2021 BASE Show details 3 Vision-and-Language or Vision-for-Language? On Cross-Modal Influence in Multimodal Transformers ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bugliarello, Emanuele; Elliott, Desmond. - : Underline Science Inc., 2021 BASE Show details 4 Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bugliarello, Emanuele; Elliott, Desmond. - : Underline Science Inc., 2021 BASE Show details 5 Multimodal pretraining unmasked: A meta-analysis and a unified framework of vision-and-language berts ... Bugliarello, Emanuele; Cotterell, Ryan; Okazaki, Naoaki. - : ETH Zurich, 2021 BASE Show details 6 mDAPT: Multilingual Domain Adaptive Pretraining in a Single Model ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Dai, Xiang; Elliott, Desmond. - : Underline Science Inc., 2021 BASE Show details 7 Visually Grounded Reasoning across Languages and Cultures ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bugliarello, Emanuele; Collier, Nigel. - : Underline Science Inc., 2021 BASE Show details 8 Visually Grounded Reasoning across Languages and Cultures ... Liu, Fangyu; Bugliarello, Emanuele; Ponti, Edoardo Maria. - : arXiv, 2021 BASE Show details 9 Multimodal pretraining unmasked: A meta-analysis and a unified framework of vision-and-language berts Okazaki, Naoaki; Bugliarello, Emanuele; Cotterell, Ryan... In: Transactions of the Association for Computational Linguistics, 9 (2021) BASE Show details 10 The Role of Syntactic Planning in Compositional Image Captioning ... Bugliarello, Emanuele; Elliott, Desmond. - : arXiv, 2021 BASE Show details 11 Visually Grounded Reasoning across Languages and Cultures ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Nigel; Bugliarello, Emanuele. - : Underline Science Inc., 2021 BASE Show details 12 CompGuessWhat?!: A Multi-task Evaluation Framework for Grounded Language Learning ... Suglia, Alessandro; Konstas, Ioannis; Vanzo, Andrea. - : arXiv, 2020 BASE Show details 13 The Sensitivity of Language Models and Humans to Winograd Schema Perturbations ... Abdou, Mostafa; Ravishankar, Vinit; Barrett, Maria; Belinkov, Yonatan; Elliott, Desmond; Søgaard, Anders. - : arXiv, 2020 Abstract: Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of common sense reasoning ability. We show, however, with a new diagnostic dataset, that these models are sensitive to linguistic perturbations of the Winograd examples that minimally affect human understanding. Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones. Overall, humans are correct more often than out-of-the-box models, and the models are sometimes right for the wrong reasons. Finally, we show that fine-tuning on a large, task-specific dataset can offer a solution to these issues. ... : ACL 2020 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.2005.01348 https://arxiv.org/abs/2005.01348 BASE Hide details 14 Bootstrapping Disjoint Datasets for Multilingual Multimodal Representation Learning ... Kádár, Ákos; Chrupała, Grzegorz; Alishahi, Afra. - : arXiv, 2019 BASE Show details 15 Cross-lingual Visual Verb Sense Disambiguation ... Gella, Spandana; Elliott, Desmond; Keller, Frank. - : arXiv, 2019 BASE Show details 16 Lessons learned in multilingual grounded language learning ... Kádár, Ákos; Elliott, Desmond; Côté, Marc-Alexandre. - : arXiv, 2018 BASE Show details 17 Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description ... Elliott, Desmond; Frank, Stella; Barrault, Loïc. - : arXiv, 2017 BASE Show details 18 Cross-linguistic differences and similarities in image descriptions ... van Miltenburg, Emiel; Elliott, Desmond; Vossen, Piek. - : arXiv, 2017 BASE Show details 19 Multi30K: Multilingual English-German Image Descriptions Elliott, Desmond [Verfasser]; Frank, Stella [Verfasser]; Sima'an, Khalil [Verfasser]. - Aachen : Universitätsbibliothek der RWTH Aachen, 2016 DNB Subject Category Language Show details 20 Multi30K: Multilingual English-German Image Descriptions ... Elliott, Desmond; Frank, Stella; Sima'an, Khalil. - : arXiv, 2016 BASE Show details Page: 1 2

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