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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 – 11 of 11 1 Input Representations for Parsing Discourse Representation Structures: Comparing English with Chinese ... 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; Bisazza, Arianna; Bos, Johan. - : Underline Science Inc., 2021 BASE Show details 2 On the Difficulty of Translating Free-Order Case-Marking Languages ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bisazza, Arianna; Sportel, Stephan. - : Underline Science Inc., 2021 BASE Show details 3 UDapter: Language Adaptation for Truly Universal Dependency Parsing ... Üstün, Ahmet; Bisazza, Arianna; Bouma, Gosse. - : arXiv, 2020 BASE Show details 4 Understanding Cross-Lingual Syntactic Transfer in Multilingual Recurrent Neural Networks ... Dhar, Prajit; Bisazza, Arianna. - : arXiv, 2020 BASE Show details 5 BERTje: A Dutch BERT Model ... de Vries, Wietse; van Cranenburgh, Andreas; Bisazza, Arianna. - : arXiv, 2019 BASE Show details 6 Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations ... Tran, Ke; Bisazza, Arianna. - : arXiv, 2019 Abstract: We investigate whether off-the-shelf deep bidirectional sentence representations trained on a massively multilingual corpus (multilingual BERT) enable the development of an unsupervised universal dependency parser. This approach only leverages a mix of monolingual corpora in many languages and does not require any translation data making it applicable to low-resource languages. In our experiments we outperform the best CoNLL 2018 language-specific systems in all of the shared task's six truly low-resource languages while using a single system. However, we also find that (i) parsing accuracy still varies dramatically when changing the training languages and (ii) in some target languages zero-shot transfer fails under all tested conditions, raising concerns on the 'universality' of the whole approach. ... : DeepLo workshop, EMNLP 2019 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1910.05479 https://arxiv.org/abs/1910.05479 BASE Hide details 7 Learning Topic-Sensitive Word Representations ... Fadaee, Marzieh; Bisazza, Arianna; Monz, Christof. - : arXiv, 2017 BASE Show details 8 Recurrent Memory Networks for Language Modeling ... Tran, Ke; Bisazza, Arianna; Monz, Christof. - : arXiv, 2016 BASE Show details 9 Neural versus Phrase-Based Machine Translation Quality: a Case Study ... Bentivogli, Luisa; Bisazza, Arianna; Cettolo, Mauro. - : arXiv, 2016 BASE Show details 10 A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena ... Bisazza, Arianna; Federico, Marcello. - : arXiv, 2015 BASE Show details 11 Chunk-lattices for verb reordering in Arabic-English statistical machine translation : special issues on machine translation for Arabic Federico, Marcello; Pighin, Daniele; Bisazza, Arianna In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 26 (2012) 1-2, 85-103 BLLDB OLC Linguistik Show details

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