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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; Reddy, Siva; Wang, Keze; Zhu, Song-Chun. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.516/ Abstract: Neural module networks (NMN) are a popular approach for grounding visual referring expressions. Prior implementations of NMN use pre-defined and fixed textual inputs in their module instantiation. This necessitates a large number of modules as they lack the ability to share weights and exploit associations between similar textual contexts (e.g. 'dark cube on the left' vs. 'black cube on the left'). In this work, we address these limitations and evaluate the impact of contextual clues in improving the performance of NMN models. First, we address the problem of fixed textual inputs by parameterizing the module arguments. This substantially reduce the number of modules in NMN by up to 75% without any loss in performance. Next we propose a method to contextualize our parameterized model to enhance the module’s capacity in exploiting the visiolinguistic associations. Our model outperforms the state-of-the-art NMN model on CLEVR-Ref+ ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Neural Network URL: https://dx.doi.org/10.48448/c8vt-s207 https://underline.io/lecture/37933-mind-the-context-the-impact-of-contextualization-in-neural-module-networks-for-grounding-visual-referring-expressions BASE Hide 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. - : arXiv, 2020 BASE Show 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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