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        <title>Neural Transition-based String Transduction for Limited-Resource Setting in Morphology</title>
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            <namePart type="given">Emily</namePart>
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    <abstract>We present a neural transition-based model that uses a simple set of edit actions (copy, delete, insert) for morphological transduction tasks such as inflection generation, lemmatization, and reinflection. In a large-scale evaluation on four datasets and dozens of languages, our approach consistently outperforms state-of-the-art systems on low and medium training-set sizes and is competitive in the high-resource setting. Learning to apply a generic copy action enables our approach to generalize quickly from a few data points. We successfully leverage minimum risk training to compensate for the weaknesses of MLE parameter learning and neutralize the negative effects of training a pipeline with a separate character aligner.</abstract>
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%0 Conference Proceedings
%T Neural Transition-based String Transduction for Limited-Resource Setting in Morphology
%A Makarov, Peter
%A Clematide, Simon
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F makarov-clematide-2018-neural
%X We present a neural transition-based model that uses a simple set of edit actions (copy, delete, insert) for morphological transduction tasks such as inflection generation, lemmatization, and reinflection. In a large-scale evaluation on four datasets and dozens of languages, our approach consistently outperforms state-of-the-art systems on low and medium training-set sizes and is competitive in the high-resource setting. Learning to apply a generic copy action enables our approach to generalize quickly from a few data points. We successfully leverage minimum risk training to compensate for the weaknesses of MLE parameter learning and neutralize the negative effects of training a pipeline with a separate character aligner.
%U https://aclanthology.org/C18-1008/
%P 83-93

Markdown (Informal)

[Neural Transition-based String Transduction for Limited-Resource Setting in Morphology](https://aclanthology.org/C18-1008/) (Makarov & Clematide, COLING 2018)

• Neural Transition-based String Transduction for Limited-Resource Setting in Morphology (Makarov & Clematide, COLING 2018)

ACL

• Peter Makarov and Simon Clematide. 2018. Neural Transition-based String Transduction for Limited-Resource Setting in Morphology. In Proceedings of the 27th International Conference on Computational Linguistics, pages 83–93, Santa Fe, New Mexico, USA. Association for Computational Linguistics.