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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 – 6 of 6 1 SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ... Vylomova, Ekaterina; White, Jennifer; Salesky, Elizabeth. - : arXiv, 2020 BASE Show details 2 SIGTYP 2020 Shared Task: Prediction of Typological Features ... Bjerva, Johannes; Salesky, Elizabeth; Mielke, Sabrina J.. - : arXiv, 2020 BASE Show details 3 Contextualization of Morphological Inflection ... Vylomova, Ekaterina; Cotterell, Ryan; Baldwin, Timothy. - : arXiv, 2019 BASE Show details 4 Context-Aware Prediction of Derivational Word-forms ... Vylomova, Ekaterina; Cotterell, Ryan; Baldwin, Timothy. - : arXiv, 2017 BASE Show details 5 Paradigm Completion for Derivational Morphology ... Cotterell, Ryan; Vylomova, Ekaterina; Khayrallah, Huda; Kirov, Christo; Yarowsky, David. - : arXiv, 2017 Abstract: The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task. We overview the theoretical motivation for a paradigmatic treatment of derivational morphology, and introduce the task of derivational paradigm completion as a parallel to inflectional paradigm completion. State-of-the-art neural models, adapted from the inflection task, are able to learn a range of derivation patterns, and outperform a non-neural baseline by 16.4%. However, due to semantic, historical, and lexical considerations involved in derivational morphology, future work will be needed to achieve performance parity with inflection-generating systems. ... : EMNLP 2017 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1708.09151 https://dx.doi.org/10.48550/arxiv.1708.09151 BASE Hide details 6 Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning ... Vylomova, Ekaterina; Rimell, Laura; Cohn, Trevor. - : arXiv, 2015 BASE Show details

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