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1
Differentiable Generative Phonology ...
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2
Applying the Transformer to Character-level Transduction ...
Wu, Shijie; Cotterell, Ryan; Hulden, Mans. - : ETH Zurich, 2021
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3
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction ...
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4
Applying the Transformer to Character-level Transduction
In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (2021)
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5
Do Explicit Alignments Robustly Improve Multilingual Encoders? ...
Wu, Shijie; Dredze, Mark. - : arXiv, 2020
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6
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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7
Are All Languages Created Equal in Multilingual BERT? ...
Wu, Shijie; Dredze, Mark. - : arXiv, 2020
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8
The Paradigm Discovery Problem ...
Erdmann, Alexander; Elsner, Micha; Wu, Shijie. - : ETH Zurich, 2020
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9
The Paradigm Discovery Problem
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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10
Emerging Cross-lingual Structure in Pretrained Language Models ...
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11
The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection ...
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
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