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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 – 7 of 7 1 Backtranslation in Neural Morphological Inflection ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Hulden, Mans; Liu, Ling. - : Underline Science Inc., 2021 BASE Show details 2 To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Hulden, Mans; Liu, Ling. - : Underline Science Inc., 2021 BASE Show details 3 Applying the Transformer to Character-level Transduction ... Wu, Shijie; Cotterell, Ryan; Hulden, Mans. - : ETH Zurich, 2021 BASE Show details 4 Do RNN States Encode Abstract Phonological Alternations? ... NAACL 2021 2021; Hulden, Mans; Nicolai, Garrett. - : Underline Science Inc., 2021 BASE Show details 5 Do RNN States Encode Abstract Phonological Processes? ... Silfverberg, Miikka; Tyers, Francis; Nicolai, Garrett. - : arXiv, 2021 BASE Show details 6 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 7 Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models ... Liu, Ling; Hulden, Mans. - : arXiv, 2021 BASE Show details

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