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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 – 4 of 4 1 SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ... Vylomova, Ekaterina; White, Jennifer; Salesky, Elizabeth. - : arXiv, 2020 BASE Show details 2 UniMorph 3.0: Universal Morphology Kirov, Christo; Nicolai, Garrett; Arkhangelskij, Timofey... In: Proceedings of the 12th Language Resources and Evaluation Conference (2020) BASE Show details 3 UniMorph 3.0: Universal Morphology ... McCarthy, Arya D.; Kirov, Christo; Grella, Matteo. - : ETH Zurich, 2020 BASE Show details 4 The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection ... McCarthy, Arya D.; Vylomova, Ekaterina; Wu, Shijie; Malaviya, Chaitanya; Wolf-Sonkin, Lawrence; Nicolai, Garrett; Kirov, Christo; Silfverberg, Miikka; Mielke, Sabrina J.; Heinz, Jeffrey; Cotterell, Ryan; Hulden, Mans. - : arXiv, 2019 Abstract: The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in morphology examined transfer learning of inflection between 100 language pairs, as well as contextual lemmatization and morphosyntactic description in 66 languages. The first task evolves past years' inflection tasks by examining transfer of morphological inflection knowledge from a high-resource language to a low-resource language. This year also presents a new second challenge on lemmatization and morphological feature analysis in context. All submissions featured a neural component and built on either this year's strong baselines or highly ranked systems from previous years' shared tasks. Every participating team improved in accuracy over the baselines for the inflection task (though not Levenshtein distance), and every team in the contextual analysis task improved on both state-of-the-art neural and non-neural baselines. ... : Presented at SIGMORPHON 2019 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1910.11493 https://dx.doi.org/10.48550/arxiv.1910.11493 BASE Hide details

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