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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? ...
Abstract: Sequence-to-sequence models have delivered impressive results in word formation tasks such as morphological inflection, often learning to model subtle morphophonological details with limited training data. Despite the performance, the opacity of neural models makes it difficult to determine whether complex generalizations are learned, or whether a kind of separate rote memorization of each morphophonological process takes place. To investigate whether complex alternations are simply memorized or whether there is some level of generalization across related sound changes in a sequence-to-sequence model, we perform several experiments on Finnish consonant gradation -- a complex set of sound changes triggered in some words by certain suffixes. We find that our models often -- though not always -- encode 17 different consonant gradation processes in a handful of dimensions in the RNN. We also show that by scaling the activations in these dimensions we can control whether consonant gradation occurs and the ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2104.00789
https://arxiv.org/abs/2104.00789
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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 ...
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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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