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        <title>A Structured Variational Autoencoder for Contextual Morphological Inflection</title>
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        <namePart type="given">Lawrence</namePart>
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            <namePart type="given">Yusuke</namePart>
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    <abstract>Statistical morphological inflectors are typically trained on fully supervised, type-level data. One remaining open research question is the following: How can we effectively exploit raw, token-level data to improve their performance? To this end, we introduce a novel generative latent-variable model for the semi-supervised learning of inflection generation. To enable posterior inference over the latent variables, we derive an efficient variational inference procedure based on the wake-sleep algorithm. We experiment on 23 languages, using the Universal Dependencies corpora in a simulated low-resource setting, and find improvements of over 10% absolute accuracy in some cases.</abstract>
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%0 Conference Proceedings
%T A Structured Variational Autoencoder for Contextual Morphological Inflection
%A Wolf-Sonkin, Lawrence
%A Naradowsky, Jason
%A Mielke, Sabrina J.
%A Cotterell, Ryan
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F wolf-sonkin-etal-2018-structured
%X Statistical morphological inflectors are typically trained on fully supervised, type-level data. One remaining open research question is the following: How can we effectively exploit raw, token-level data to improve their performance? To this end, we introduce a novel generative latent-variable model for the semi-supervised learning of inflection generation. To enable posterior inference over the latent variables, we derive an efficient variational inference procedure based on the wake-sleep algorithm. We experiment on 23 languages, using the Universal Dependencies corpora in a simulated low-resource setting, and find improvements of over 10% absolute accuracy in some cases.
%R 10.18653/v1/P18-1245
%U https://aclanthology.org/P18-1245
%U https://doi.org/10.18653/v1/P18-1245
%P 2631-2641

Markdown (Informal)

[A Structured Variational Autoencoder for Contextual Morphological Inflection](https://aclanthology.org/P18-1245) (Wolf-Sonkin et al., ACL 2018)

• A Structured Variational Autoencoder for Contextual Morphological Inflection (Wolf-Sonkin et al., ACL 2018)

ACL

• Lawrence Wolf-Sonkin, Jason Naradowsky, Sabrina J. Mielke, and Ryan Cotterell. 2018. A Structured Variational Autoencoder for Contextual Morphological Inflection. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2631–2641, Melbourne, Australia. Association for Computational Linguistics.