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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) BASE Show details 5 Do Explicit Alignments Robustly Improve Multilingual Encoders? ... Wu, Shijie; Dredze, Mark. - : arXiv, 2020 Abstract: Multilingual BERT (mBERT), XLM-RoBERTa (XLMR) and other unsupervised multilingual encoders can effectively learn cross-lingual representation. Explicit alignment objectives based on bitexts like Europarl or MultiUN have been shown to further improve these representations. However, word-level alignments are often suboptimal and such bitexts are unavailable for many languages. In this paper, we propose a new contrastive alignment objective that can better utilize such signal, and examine whether these previous alignment methods can be adapted to noisier sources of aligned data: a randomly sampled 1 million pair subset of the OPUS collection. Additionally, rather than report results on a single dataset with a single model run, we report the mean and standard derivation of multiple runs with different seeds, on four datasets and tasks. Our more extensive analysis finds that, while our new objective outperforms previous work, overall these methods do not improve performance with a more robust evaluation ... : EMNLP 2020 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2010.02537 https://dx.doi.org/10.48550/arxiv.2010.02537 BASE Hide 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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