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        <title>Modeling Composite Labels for Neural Morphological Tagging</title>
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        <namePart type="given">Alexander</namePart>
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        <namePart type="given">Kairit</namePart>
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            <title>Proceedings of the 22nd Conference on Computational Natural Language Learning</title>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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        <name type="personal">
            <namePart type="given">Ivan</namePart>
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    <abstract>Neural morphological tagging has been regarded as an extension to POS tagging task, treating each morphological tag as a monolithic label and ignoring its internal structure. We propose to view morphological tags as composite labels and explicitly model their internal structure in a neural sequence tagger. For this, we explore three different neural architectures and compare their performance with both CRF and simple neural multiclass baselines. We evaluate our models on 49 languages and show that the neural architecture that models the morphological labels as sequences of morphological category values performs significantly better than both baselines establishing state-of-the-art results in morphological tagging for most languages.</abstract>
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    <identifier type="doi">10.18653/v1/K18-1036</identifier>
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%0 Conference Proceedings
%T Modeling Composite Labels for Neural Morphological Tagging
%A Tkachenko, Alexander
%A Sirts, Kairit
%Y Korhonen, Anna
%Y Titov, Ivan
%S Proceedings of the 22nd Conference on Computational Natural Language Learning
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F tkachenko-sirts-2018-modeling
%X Neural morphological tagging has been regarded as an extension to POS tagging task, treating each morphological tag as a monolithic label and ignoring its internal structure. We propose to view morphological tags as composite labels and explicitly model their internal structure in a neural sequence tagger. For this, we explore three different neural architectures and compare their performance with both CRF and simple neural multiclass baselines. We evaluate our models on 49 languages and show that the neural architecture that models the morphological labels as sequences of morphological category values performs significantly better than both baselines establishing state-of-the-art results in morphological tagging for most languages.
%R 10.18653/v1/K18-1036
%U https://aclanthology.org/K18-1036
%U https://doi.org/10.18653/v1/K18-1036
%P 368-379

Markdown (Informal)

[Modeling Composite Labels for Neural Morphological Tagging](https://aclanthology.org/K18-1036) (Tkachenko & Sirts, CoNLL 2018)

• Modeling Composite Labels for Neural Morphological Tagging (Tkachenko & Sirts, CoNLL 2018)

ACL

• Alexander Tkachenko and Kairit Sirts. 2018. Modeling Composite Labels for Neural Morphological Tagging. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 368–379, Brussels, Belgium. Association for Computational Linguistics.