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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 – 3 of 3 1 The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures ... Dubossarsky, Haim; Vulić, Ivan; Reichart, Roi. - : arXiv, 2020 BASE Show details 2 The Secret is in the Spectra: Predicting Cross-Lingual Task Performance with Spectral Similarity Measures ... Dubossarsky, Haim; Vulic, Ivan; Reichart, Roi. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 3 The Secret is in the Spectra: Predicting Cross-Lingual Task Performance with Spectral Similarity Measures Dubossarsky, Haim; Vulic, Ivan; Reichart, Roi; Korhonen, Anna-Leena. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2020 Abstract: Performance in cross-lingual NLP tasks is impacted by the (dis)similarity of languages at hand: e.g., previous work has suggested there is a connection between the expected success of bilingual lexicon induction (BLI) and the assumption of (approximate) isomorphism between monolingual embedding spaces. In this work we present a large-scale study focused on the correlations between monolingual embedding space similarity and task performance, covering thousands of language pairs and four different tasks: BLI, parsing, POS tagging and MT. We hypothesize that statistics of the spectrum of each monolingual embedding space indicate how well they can be aligned. We then introduce several isomorphism measures between two embedding spaces, based on the relevant statistics of their individual spectra. We empirically show that 1) language similarity scores derived from such spectral isomorphism measures are strongly associated with performance observed in different cross-lingual tasks, and 2) our spectral-based measures consistently outperform previous standard isomorphism measures, while being computationally more tractable and easier to interpret. Finally, our measures capture complementary information to typologically driven language distance measures, and the combination of measures from the two families yields even higher task performance correlations. URL: https://doi.org/10.17863/CAM.62208 https://www.repository.cam.ac.uk/handle/1810/315101 BASE Hide details

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