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        <title>Imitation Learning for Neural Morphological String Transduction</title>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
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            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>We employ imitation learning to train a neural transition-based string transducer for morphological tasks such as inflection generation and lemmatization. Previous approaches to training this type of model either rely on an external character aligner for the production of gold action sequences, which results in a suboptimal model due to the unwarranted dependence on a single gold action sequence despite spurious ambiguity, or require warm starting with an MLE model. Our approach only requires a simple expert policy, eliminating the need for a character aligner or warm start. It also addresses familiar MLE training biases and leads to strong and state-of-the-art performance on several benchmarks.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1314</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Imitation Learning for Neural Morphological String Transduction
%A Makarov, Peter
%A Clematide, Simon
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F makarov-clematide-2018-imitation
%X We employ imitation learning to train a neural transition-based string transducer for morphological tasks such as inflection generation and lemmatization. Previous approaches to training this type of model either rely on an external character aligner for the production of gold action sequences, which results in a suboptimal model due to the unwarranted dependence on a single gold action sequence despite spurious ambiguity, or require warm starting with an MLE model. Our approach only requires a simple expert policy, eliminating the need for a character aligner or warm start. It also addresses familiar MLE training biases and leads to strong and state-of-the-art performance on several benchmarks.
%R 10.18653/v1/D18-1314
%U https://aclanthology.org/D18-1314
%U https://doi.org/10.18653/v1/D18-1314
%P 2877-2882

Markdown (Informal)

[Imitation Learning for Neural Morphological String Transduction](https://aclanthology.org/D18-1314) (Makarov & Clematide, EMNLP 2018)

• Imitation Learning for Neural Morphological String Transduction (Makarov & Clematide, EMNLP 2018)

ACL

• Peter Makarov and Simon Clematide. 2018. Imitation Learning for Neural Morphological String Transduction. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2877–2882, Brussels, Belgium. Association for Computational Linguistics.