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    • Wu, Shijie (11)
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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 – 11 of 11 1 Differentiable Generative Phonology ... Wu, Shijie; Ponti, Edoardo Maria; Cotterell, Ryan. - : arXiv, 2021 BASE Show details 2 Applying the Transformer to Character-level Transduction ... Wu, Shijie; Cotterell, Ryan; Hulden, Mans. - : ETH Zurich, 2021 BASE Show details 3 Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction ... Yarmohammadi, Mahsa; Wu, Shijie; Marone, Marc. - : arXiv, 2021 BASE Show details 4 Applying the Transformer to Character-level Transduction Cotterell, Ryan; Hulden, Mans; Wu, Shijie In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (2021) Abstract: The transformer has been shown to outperform recurrent neural network-based sequence-to-sequence models in various word-level NLP tasks. Yet for character-level transduction tasks, e.g. morphological inflection generation and historical text normalization, there are few works that outperform recurrent models using the transformer. In an empirical study, we uncover that, in contrast to recurrent sequence-to-sequence models, the batch size plays a crucial role in the performance of the transformer on character-level tasks, and we show that with a large enough batch size, the transformer does indeed outperform recurrent models. We also introduce a simple technique to handle feature-guided character-level transduction that further improves performance. With these insights, we achieve state-of-the-art performance on morphological inflection and historical text normalization. We also show that the transformer outperforms a strong baseline on two other character-level transduction tasks: grapheme-to-phoneme conversion and transliteration. URL: https://hdl.handle.net/20.500.11850/518998 https://doi.org/10.3929/ethz-b-000518998 BASE Hide details 5 Do Explicit Alignments Robustly Improve Multilingual Encoders? ... Wu, Shijie; Dredze, Mark. - : arXiv, 2020 BASE Show details 6 SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ... Vylomova, Ekaterina; White, Jennifer; Salesky, Elizabeth. - : arXiv, 2020 BASE Show details 7 Are All Languages Created Equal in Multilingual BERT? ... Wu, Shijie; Dredze, Mark. - : arXiv, 2020 BASE Show details 8 The Paradigm Discovery Problem ... Erdmann, Alexander; Elsner, Micha; Wu, Shijie. - : ETH Zurich, 2020 BASE Show details 9 The Paradigm Discovery Problem Wu, Shijie; Habash, Nizar; Elsner, Micha... In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020) BASE Show details 10 Emerging Cross-lingual Structure in Pretrained Language Models ... Wu, Shijie; Conneau, Alexis; Li, Haoran. - : arXiv, 2019 BASE Show details 11 The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection ... McCarthy, Arya D.; Vylomova, Ekaterina; Wu, Shijie. - : arXiv, 2019 BASE Show details

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