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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 BASE Show 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 Abstract: Recurrent Neural Networks (RNN) have obtained excellent result in many natural language processing (NLP) tasks. However, understanding and interpreting the source of this success remains a challenge. In this paper, we propose Recurrent Memory Network (RMN), a novel RNN architecture, that not only amplifies the power of RNN but also facilitates our understanding of its internal functioning and allows us to discover underlying patterns in data. We demonstrate the power of RMN on language modeling and sentence completion tasks. On language modeling, RMN outperforms Long Short-Term Memory (LSTM) network on three large German, Italian, and English dataset. Additionally we perform in-depth analysis of various linguistic dimensions that RMN captures. On Sentence Completion Challenge, for which it is essential to capture sentence coherence, our RMN obtains 69.2% accuracy, surpassing the previous state-of-the-art by a large margin. ... : 8 pages, 6 figures. Accepted at NAACL 2016 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1601.01272 https://arxiv.org/abs/1601.01272 BASE Hide 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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