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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. - : 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) Abstract: Humans gather information through conversations involving a series of interconnected questions and answers. For machines to assist in information gathering, it is therefore essential to enable them to answer conversational questions. We introduce CoQA, a novel dataset for building Conversational Question Answering systems. Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA in depth and show that conversational questions have challenging phenomena not present in existing reading comprehension datasets (e.g., coreference and pragmatic reasoning). We evaluate strong dialogue and reading comprehension models on CoQA. The best system obtains an F1 score of 65.4%, which is 23.4 points behind human performance (88.8%), indicating that there is ample room for improvement. We present CoQA as a challenge to the community at https://stanfordnlp.github.io/coqa . Keyword: Computational linguistics. Natural language processing; P98-98.5 URL: https://doaj.org/article/d56a691b0e77463d8a1065056086cf9b https://doi.org/10.1162/tacl_a_00266 BASE Hide 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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