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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 39 1 IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages ... Bugliarello, Emanuele; Liu, Fangyu; Pfeiffer, Jonas. - : arXiv, 2022 BASE Show details 2 Universal Dependencies 2.9 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 3 Universal Dependencies 2.8.1 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 4 Universal Dependencies 2.8 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 5 Modelling Latent Translations for Cross-Lingual Transfer ... Ponti, Edoardo Maria; Kreutzer, Julia; Vulić, Ivan. - : arXiv, 2021 BASE Show details 6 Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Aralikatte, Rahul; Maria Ponti, Edoardo. - : Underline Science Inc., 2021 BASE Show details 7 Mind the Context: The Impact of Contextualization in Neural Module Networks for Grounding Visual Referring Expressions ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Akula, Arjun; Gella, Spandana. - : Underline Science Inc., 2021 BASE Show details 8 Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Belfer, Robert; Kulshreshtha, Devang. - : Underline Science Inc., 2021 BASE Show details 9 Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Aralikatte, Rahul; Maria Ponti, Edoardo. - : Underline Science Inc., 2021 BASE Show details 10 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 11 Visually Grounded Reasoning across Languages and Cultures ... Liu, Fangyu; Bugliarello, Emanuele; Ponti, Edoardo Maria. - : arXiv, 2021 BASE Show details 12 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 13 Universal Dependencies 2.7 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 14 Universal Dependencies 2.6 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 15 Words aren't enough, their order matters: On the Robustness of Grounding Visual Referring Expressions ... Akula, Arjun R; Gella, Spandana; Al-Onaizan, Yaser; Zhu, Song-Chun; Reddy, Siva. - : arXiv, 2020 Abstract: Visual referring expression recognition is a challenging task that requires natural language understanding in the context of an image. We critically examine RefCOCOg, a standard benchmark for this task, using a human study and show that 83.7% of test instances do not require reasoning on linguistic structure, i.e., words are enough to identify the target object, the word order doesn't matter. To measure the true progress of existing models, we split the test set into two sets, one which requires reasoning on linguistic structure and the other which doesn't. Additionally, we create an out-of-distribution dataset Ref-Adv by asking crowdworkers to perturb in-domain examples such that the target object changes. Using these datasets, we empirically show that existing methods fail to exploit linguistic structure and are 12% to 23% lower in performance than the established progress for this task. We also propose two methods, one based on contrastive learning and the other based on multi-task learning, to increase ... : ACL 2020 ... Keyword: Computation and Language cs.CL; Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences URL: https://arxiv.org/abs/2005.01655 https://dx.doi.org/10.48550/arxiv.2005.01655 BASE Hide details 16 MeDAL ... Wen, Zhi; Lu, Xing Han; Reddy, Siva. - : Zenodo, 2020 BASE Show details 17 Universal Dependencies 2.5 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019 BASE Show details 18 Universal Dependencies 2.4 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019 BASE Show details 19 CoQA: A Conversational Question Answering Challenge Reddy, Siva; Chen, Danqi; Manning, Christopher D. In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 249-266 (2019) (2019) BASE Show details 20 Universal Dependencies 2.2 Nivre, Joakim; Abrams, Mitchell; Agić, Željko... In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018) BASE Show details Page: 1 2

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