A Structured Variational Autoencoder for Contextual Morphological Inflection

Wolf-Sonkin, Lawrence; Naradowsky, Jason; Mielke, Sebastian J.; Cotterell, Ryan

doi:10.18653/v1/p18-1245

Cited by 5 publications

(8 citation statements)

References 29 publications

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“…This neural model did not surpass the CRF model with their designed features. The results of and Moeller and Hulden (2018) indicate that for conditions where we have extremely limited amount of labeled data, nonneural models with linguistic feature engineering still have an advantage, a finding also supported by Wolf-Sonkin et al (2018) for morphological inflection within context. Cotterell and Schütze (2018) incorporate semantics into segmentation.…”

Section: Surface Morphological Segmentationmentioning

confidence: 79%

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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Section: Surface Morphological Segmentationmentioning

confidence: 79%

Computational Morphology with Neural Network Approaches

Liu¹

2021

Preprint

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“…For our purposes the existing approaches can be characterised by their application scenarios and assumptions about available datasets. Interesting work has been done within the neural, supervised and semi-supervised frameworks, e.g., (Ahlberg et al, 2015), (Ahlberg et al, 2014), (Koskenniemi et al, 2018), (Silfverberg et al, 2018), (Wolf-Sonkin et al, 2018), (Kirov and Cotterell, 2018), (Faruqui et al, 2016), (Faruqui et al, 2015), (Aharoni and Goldberg, 2016), (Cotterell et al, 2017). Much of this work assumes availability of partially labelled data, such as word paradigms and/or clean datasets, such as lists of 'headwords' (lemmas) from which paradigms are generated.…”

Section: Previous Workmentioning

confidence: 99%

Unsupervised Induction of Ukrainian Morphological Paradigms for the New Lexicon: Extending Coverage for Named Entities and Neologisms using Inflection Tables and Unannotated Corpora

Babych

2019

Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing

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The paper presents an unsupervised method for quickly extending a Ukrainian lexicon by generating paradigms and morphological feature structures for new proper names and neologisms, which are not covered by existing static morphological resources. This approach addresses a practical problem of modelling paradigms for entities created by the dynamic processes in the lexicon: this problem is especially serious for highly-inflected languages in domains with specialised or quickly changing lexicon. The method uses an unannotated Ukrainian corpus and a small fixed set of inflection tables, which can be found in traditional grammar textbooks. The advantage of the proposed approach is that updating the morphological lexicon does not require training or linguistic annotation, allowing fast knowledge-light extension of an existing static lexicon to improve morphological coverage on a specific corpus. The method is implemented in an open-source package on a GitHub repository. It can be applied to other low-resourced inflectional languages which have internet corpora and linguistic descriptions of their inflection system, following the example of inflection tables for Ukrainian. Evaluation results show consistent improvements in coverage for Ukrainian corpora of different corpus types.

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“…Inflection generation in context is a novel SIGMORPHON challenge and, generally, a less studied problem. Recently, Wolf-Sonkin et al (2018) propose a context-aware deep generative graphical model that generates sequences of inflected words.…”

Section: State Of the Artmentioning

confidence: 99%

“…The CoNLL-SIGMORPHON 2018 Shared Task on Universal Morphological Reinflection (Cotterell et al, 2018) focuses on inflection generation at the type level (Task I) and in context (Task II). Both tasks feature three settings depending on the maximum number of training examples: low, medium, and high.…”

Section: Introductionmentioning

confidence: 99%

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Makarov

Clematide

2018

Proceedings of The

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This paper presents the submissions by the University of Zurich to the CoNLL-SIGMORPHON 2018 Shared Task on Universal Morphological Reinflection. Our system is based on the prior work on neural transitionbased transduction (Makarov and Clematide, 2018b; Aharoni and Goldberg, 2017). Unlike the prior work, we train the model in a fully end-to-end fashion-without the need for an external character aligner-within the framework of imitation learning. In the type-level morphological inflection generation challenge (Task I), our five-strong ensemble outperforms all competitors in all three data-size settings. In the token-level inflection generation challenge (Task II), our single model achieves the best results on three out of four sub-tasks that we have participated in.

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A Structured Variational Autoencoder for Contextual Morphological Inflection

Cited by 5 publications

References 29 publications

Computational Morphology with Neural Network Approaches

Computational Morphology with Neural Network Approaches

Unsupervised Induction of Ukrainian Morphological Paradigms for the New Lexicon: Extending Coverage for Named Entities and Neologisms using Inflection Tables and Unannotated Corpora

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