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1
Backtranslation in Neural Morphological Inflection ...
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2
To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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3
Applying the Transformer to Character-level Transduction ...
Wu, Shijie; Cotterell, Ryan; Hulden, Mans. - : ETH Zurich, 2021
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4
Do RNN States Encode Abstract Phonological Alternations? ...
NAACL 2021 2021; Hulden, Mans; Nicolai, Garrett. - : Underline Science Inc., 2021
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5
Do RNN States Encode Abstract Phonological Processes? ...
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6
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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7
Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models ...
Liu, Ling; Hulden, Mans. - : arXiv, 2021
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8
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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9
Analogy Models for Neural Word Inflection ...
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10
UniMorph 3.0: Universal Morphology
In: Proceedings of the 12th Language Resources and Evaluation Conference (2020)
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11
UniMorph 3.0: Universal Morphology ...
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12
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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13
RNN Classification of English Vowels: Nasalized or Not
In: Proceedings of the Society for Computation in Linguistics (2019)
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14
On the Complexity and Typology of Inflectional Morphological Systems
In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 327-342 (2019) (2019)
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15
Marrying Universal Dependencies and Universal Morphology ...
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16
On the Complexity and Typology of Inflectional Morphological Systems ...
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17
Sound Analogies with Phoneme Embeddings
In: Proceedings of the Society for Computation in Linguistics (2018)
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18
Quantifying the Trade-off Between Two Types of Morphological Complexity
In: Proceedings of the Society for Computation in Linguistics (2018)
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19
A Comparison of Feature-Based and Neural Scansion of Poetry ...
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20
Foma: a finite-state compiler and library
In: Association for Computational Linguistics / European Chapter. Conference of the European Chapter of the Association for Computational Linguistics. - Menlo Park, Calif. : ACL 12 (2009), 29-32
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