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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 – 8 of 8 1 Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP Mielke, Sabrina J.; Alyafeai, Zaid; Salesky, Elizabeth... In: https://hal.inria.fr/hal-03540069 ; 2022 (2022) BASE Show details 2 SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ... Vylomova, Ekaterina; White, Jennifer; Salesky, Elizabeth. - : arXiv, 2020 BASE Show details 3 SIGTYP 2020 Shared Task: Prediction of Typological Features ... Bjerva, Johannes; Salesky, Elizabeth; Mielke, Sabrina J.. - : arXiv, 2020 BASE Show details 4 Linguistic calibration through metacognition: aligning dialogue agent responses with expected correctness ... Mielke, Sabrina J.; Szlam, Arthur; Boureau, Y-Lan. - : arXiv, 2020 BASE Show details 5 Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset ... Roark, Brian; Wolf-Sonkin, Lawrence; Kirov, Christo. - : arXiv, 2020 BASE Show details 6 Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model ... Mielke, Sabrina J.; Eisner, Jason. - : arXiv, 2018 BASE Show details 7 Are All Languages Equally Hard to Language-Model? ... Cotterell, Ryan; Mielke, Sabrina J.; Eisner, Jason. - : arXiv, 2018 BASE Show details 8 Unsupervised Disambiguation of Syncretism in Inflected Lexicons ... Cotterell, Ryan; Kirov, Christo; Mielke, Sabrina J.; Eisner, Jason. - : arXiv, 2018 Abstract: Lexical ambiguity makes it difficult to compute various useful statistics of a corpus. A given word form might represent any of several morphological feature bundles. One can, however, use unsupervised learning (as in EM) to fit a model that probabilistically disambiguates word forms. We present such an approach, which employs a neural network to smoothly model a prior distribution over feature bundles (even rare ones). Although this basic model does not consider a token's context, that very property allows it to operate on a simple list of unigram type counts, partitioning each count among different analyses of that unigram. We discuss evaluation metrics for this novel task and report results on 5 languages. ... : Published at NAACL 2018 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1806.03740 https://dx.doi.org/10.48550/arxiv.1806.03740 BASE Hide details

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