Morphological Reinflection in Context:

Liu, Ling; Subbiah, Ilamvazhuthy; Wiemerslage, Adam; Lilley, Jonathan; Moeller, Sarah

doi:10.18653/v1/k18-3010

Cited by 3 publications

References 7 publications

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Cotterell¹,

Kirov²,

Sylak-Glassman³

et al. 2018

Proceedings of The

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The CoNLL-SIGMORPHON 2018 shared task on supervised learning of morphological generation featured data sets from 103 typologically diverse languages. Apart from extending the number of languages involved in earlier supervised tasks of generating inflected forms, this year the shared task also featured a new second task which asked participants to inflect words in sentential context, similar to a cloze task. This second task featured seven languages. Task 1 received 27 submissions and task 2 received 6 submissions. Both tasks featured a low, medium, and high data condition. Nearly all submissions featured a neural component and built on highly-ranked systems from the earlier 2017 shared task. In the inflection task (task 1), 41 of the 52 languages present in last year's inflection task showed improvement by the best systems in the low-resource setting. The cloze task (task 2) proved to be difficult, and few submissions managed to consistently improve upon both a simple neural baseline system and a lemmarepeating baseline.

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The

Cotterell¹,

Kirov²,

Sylak-Glassman³

et al. 2018

Proceedings of The

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Improving Low-Resource Morphological Learning with Intermediate Forms from Finite State Transducers

et al. 2019

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Neural encoder-decoder models are usually applied to morphology learning as an end-to-end process without considering the underlying phonological representations that linguists posit as abstract forms before morphophonological rules are applied. Finite State Transducers for morphology, on the other hand, are developed to contain these underlying forms as an intermediate representation. This paper shows that training a bidirectional two-step encoder-decoder model of Arapaho verbs to learn two separate mappings between tags and abstract morphemes and morphemes and surface allomorphs improves results when training data is limited to 10,000 to 30,000 examples of inflected word forms.

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Computational Morphology with Neural Network Approaches

Liu¹

2021

Preprint

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Neural network approaches have been applied to computational morphology with great success, improving the performance of most tasks by a large margin and providing new perspectives for modeling. This paper starts with a brief introduction to computational morphology, followed by a review of recent work on computational morphology with neural network approaches, to provide an overview of the area. In the end, we will analyze the advantages and problems of neural network approaches to computational morphology, and point out some directions to be explored by future research and study.

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Morphological Reinflection in Context:

Cited by 3 publications

References 7 publications

The

The

Improving Low-Resource Morphological Learning with Intermediate Forms from Finite State Transducers

Computational Morphology with Neural Network Approaches

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