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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 – 14 of 14 1 Lightweight Cross-Lingual Sentence Representation Learning ... 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; Chu, Chenhui; Gupta, Prakhar. - : Underline Science Inc., 2021 BASE Show details 2 Obtaining Better Static Word Embeddings Using Contextual Embedding Models ... Gupta, Prakhar; Jaggi, Martin. - : arXiv, 2021 BASE Show details 3 Obtaining Better Static Word Embeddings Using Contextual Embedding Models ... 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; Gupta, Prakhar; Jaggi, Martin. - : Underline Science Inc., 2021 BASE Show details 4 Correlating Twitter Language with Community-Level Health Outcomes Jaggi, Martin; Grubenmann, Ralf; Logean, Severine Rion... In: http://infoscience.epfl.ch/record/278185 (2020) BASE Show details 5 Robust Cross-lingual Embeddings from Parallel Sentences ... Sabet, Ali; Gupta, Prakhar; Cordonnier, Jean-Baptiste; West, Robert; Jaggi, Martin. - : arXiv, 2019 Abstract: Recent advances in cross-lingual word embeddings have primarily relied on mapping-based methods, which project pretrained word embeddings from different languages into a shared space through a linear transformation. However, these approaches assume word embedding spaces are isomorphic between different languages, which has been shown not to hold in practice (Søgaard et al., 2018), and fundamentally limits their performance. This motivates investigating joint learning methods which can overcome this impediment, by simultaneously learning embeddings across languages via a cross-lingual term in the training objective. We propose a bilingual extension of the CBOW method which leverages sentence-aligned corpora to obtain robust cross-lingual word and sentence representations. Our approach significantly improves cross-lingual sentence retrieval performance over all other approaches while maintaining parity with the current state-of-the-art methods on word-translation. It also achieves parity with a deep RNN method ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Information Retrieval cs.IR; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.1912.12481 https://arxiv.org/abs/1912.12481 BASE Hide details 6 Crosslingual Document Embedding as Reduced-Rank Ridge Regression ... Josifoski, Martin; Paskov, Ivan S.; Paskov, Hristo S.. - : arXiv, 2019 BASE Show details 7 System-Aware Algorithms For Machine Learning Mendler-Dünner, Celestine. - : ETH Zurich, 2019 BASE Show details 8 Crosslingual Document Embedding as Reduced-Rank Ridge Regression Paskov, Ivan S.; West, Robert; Paskov, Hristo S.... In: http://infoscience.epfl.ch/record/263893 (2019) BASE Show details 9 Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification ... Deriu, Jan; Lucchi, Aurelien; De Luca, Valeria. - : arXiv, 2017 BASE Show details 10 Generating Steganographic Text with LSTMs ... Fang, Tina; Jaggi, Martin; Argyraki, Katerina. - : arXiv, 2017 BASE Show details 11 Leveraging large amounts of weakly supervised data for multi-language sentiment classification ... Deriu, Jan Milan; Lucchi, Aurelien; De Luca, Valeria. - : ACM, 2017 BASE Show details 12 Leveraging large amounts of weakly supervised data for multi-language sentiment classification ... Deriu, Jan; Lucchi, Aurélien; De Luca, Valeria. - : ETH Zurich, 2017 BASE Show details 13 Generating Steganographic Text with LSTMs Tina Fang, Tina; Jaggi, Martin; Argyraki, Katerina In: http://infoscience.epfl.ch/record/229881 (2017) BASE Show details 14 Swiss-chocolate : sentiment detection using sparse SVMs and part-of-speech n-grams ... Jaggi, Martin; Uzdilli, Fatih; Cieliebak, Mark. - : Association for Computational Linguistics, 2014 BASE Show details

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