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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 Hits 1 – 12 of 12 1 "Wikily" Supervised Neural Translation Tailored to Cross-Lingual Tasks ... Rasooli, Mohammad Sadegh; Callison-Burch, Chris; Wijaya, Derry Tanti. - : arXiv, 2021 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 Wikily Supervised Neural Translation Tailored to Cross-Lingual Tasks ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Callison-Burch, Chris; Rasooli, Mohammad Sadegh. - : Underline Science Inc., 2021 BASE Show details 6 Universal Dependencies 2.7 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 7 ParsiNLU: A Suite of Language Understanding Challenges for Persian ... Khashabi, Daniel; Cohan, Arman; Shakeri, Siamak. - : arXiv, 2020 BASE Show details 8 The Persian Dependency Treebank Made Universal ... Rasooli, Mohammad Sadegh; Safari, Pegah; Moloodi, Amirsaeid. - : arXiv, 2020 BASE Show details 9 Mutlitask Learning for Cross-Lingual Transfer of Semantic Dependencies ... Aminian, Maryam; Rasooli, Mohammad Sadegh; Diab, Mona. - : arXiv, 2020 Abstract: We describe a method for developing broad-coverage semantic dependency parsers for languages for which no semantically annotated resource is available. We leverage a multitask learning framework coupled with an annotation projection method. We transfer supervised semantic dependency parse annotations from a rich-resource language to a low-resource language through parallel data, and train a semantic parser on projected data. We make use of supervised syntactic parsing as an auxiliary task in a multitask learning framework, and show that with different multitask learning settings, we consistently improve over the single-task baseline. In the setting in which English is the source, and Czech is the target language, our best multitask model improves the labeled F1 score over the single-task baseline by 1.8 in the in-domain SemEval data (Oepen et al., 2015), as well as 2.5 in the out-of-domain test set. Moreover, we observe that syntactic and semantic dependency direction match is an important factor in ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2004.14961 https://dx.doi.org/10.48550/arxiv.2004.14961 BASE Hide details 10 Cross-Lingual Transfer of Natural Language Processing Systems Rasooli, Mohammad Sadegh. - 2019 BASE Show details 11 Cross-Lingual Transfer of Semantic Roles: From Raw Text to Semantic Roles ... Aminian, Maryam; Rasooli, Mohammad Sadegh; Diab, Mona. - : arXiv, 2019 BASE Show details 12 Cross-Lingual Syntactic Transfer with Limited Resources ... Rasooli, Mohammad Sadegh; Collins, Michael. - : arXiv, 2016 BASE Show details

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