When more is more: The mixed language Light Warlpiri amalgamates source language phonologies to form a near-maximal inventory

Bundgaard‐Nielsen, Rikke L.; O'Shannessy, Carmel

doi:10.1016/j.wocn.2021.101037

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…The processing result of each module affects the performance of the final machine translation system. With the development of deep learning technology, deep learning has gradually been adopted to train multilevel neural networks to perform specified tasks [9][10]. Progress has been made in processing natural language, such as machine translation, question-andanswer systems, reading, and comprehension.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic Translation Correction Mechanisms: An End-to-end Algorithmic Implementation of English-Chinese Machine Translation

Shi

2023

ICST Transactions on Scalable Information Systemst

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INTRODUCTION: Machine translation is a modern natural language processing research field with important scientific and practical significance. In practice, the variation of languages, the limitation of semantic knowledge, and the lack of parallel language resources limit the development of machine translation. OBJECTIVES: This paper aims to avoid duplicating neural networks during the learning process and improve the ability to generalize complex neural network machine translation models with limited resources. METHODS: Textual material in the source language was studied, and a suitable textual material representation model was used to express complex, high-level, and abstract semantic information. Then, a more efficient neural network machine translation integration model was developed based on the control of written data and algorithms. RESULTS: Data mining must be applied to complex neural network machine translation systems based on transfer learning to standardize finite neural network models. CONCLUSION: Neural network-based embedded machine translation systems based on migration training require a small number of labelled samples to improve the system's permeability. However, this adaptive migration learning region approach can easily lead to over-learning problems in neural network machine translation models, thus avoiding excessive correspondences during the learning process and improving the generalization ability of the translation model with limited neural network resources.

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Section: Introductionmentioning

confidence: 99%

Algorithmic Translation Correction Mechanisms: An End-to-end Algorithmic Implementation of English-Chinese Machine Translation

Shi

2023

ICST Transactions on Scalable Information Systemst

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Stop contrast acquisition in child Kriol: Evidence of stable transmission of phonology post Creole formation

et al. 2023

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Many Aboriginal Australian communities are undergoing language shift from traditional Indigenous languages to contact varieties such as Kriol, an English-lexified Creole. Kriol is reportedly characterised by lexical items with highly variable phonological specifications, and variable implementation of voicing and manner contrasts in obstruents (Sandefur, 1986). A language, such as Kriol, characterised by this unusual degree of variability presents Kriol-acquiring children with a potentially difficult language-learning task, and one which challenges the prevalent theories of acquisition. To examine stop consonant acquisition in this unusual language environment, we present a study of Kriol stop and affricate production, followed by a mispronunciation detection study, with Kriol-speaking children (ages 4-7) from a Northern Territory community where Kriol is the lingua franca. In contrast to previous claims, the results suggest that Kriol-speaking children acquire a stable phonology and lexemes with canonical phonemic specifications, and that English experience would not appear to induce this stability.

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Production and perception of stop voicing in Central Australian Aboriginal English: A cross-generational study

Wang,

O’Shannessy,

Davis

et al. 2024

Australian Journal of Linguistics

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When more is more: The mixed language Light Warlpiri amalgamates source language phonologies to form a near-maximal inventory

Cited by 6 publications

References 17 publications

Algorithmic Translation Correction Mechanisms: An End-to-end Algorithmic Implementation of English-Chinese Machine Translation

Algorithmic Translation Correction Mechanisms: An End-to-end Algorithmic Implementation of English-Chinese Machine Translation

Stop contrast acquisition in child Kriol: Evidence of stable transmission of phonology post Creole formation

Production and perception of stop voicing in Central Australian Aboriginal English: A cross-generational study

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