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        <title>Korean Morphological Analysis with Tied Sequence-to-Sequence Multi-Task Model</title>
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        <namePart type="given">Hyun-Je</namePart>
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            <title>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</title>
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            <namePart type="given">Kentaro</namePart>
            <namePart type="family">Inui</namePart>
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        <name type="personal">
            <namePart type="given">Jing</namePart>
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        <name type="personal">
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            <namePart type="given">Xiaojun</namePart>
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    <abstract>Korean morphological analysis has been considered as a sequence of morpheme processing and POS tagging. Thus, a pipeline model of the tasks has been adopted widely by previous studies. However, the model has a problem that it cannot utilize interactions among the tasks. This paper formulates Korean morphological analysis as a combination of the tasks and presents a tied sequence-to-sequence multi-task model for training the two tasks simultaneously without any explicit regularization. The experiments prove the proposed model achieves the state-of-the-art performance.</abstract>
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    <identifier type="doi">10.18653/v1/D19-1150</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Korean Morphological Analysis with Tied Sequence-to-Sequence Multi-Task Model
%A Song, Hyun-Je
%A Park, Seong-Bae
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F song-park-2019-korean
%X Korean morphological analysis has been considered as a sequence of morpheme processing and POS tagging. Thus, a pipeline model of the tasks has been adopted widely by previous studies. However, the model has a problem that it cannot utilize interactions among the tasks. This paper formulates Korean morphological analysis as a combination of the tasks and presents a tied sequence-to-sequence multi-task model for training the two tasks simultaneously without any explicit regularization. The experiments prove the proposed model achieves the state-of-the-art performance.
%R 10.18653/v1/D19-1150
%U https://aclanthology.org/D19-1150/
%U https://doi.org/10.18653/v1/D19-1150
%P 1436-1441

Markdown (Informal)

[Korean Morphological Analysis with Tied Sequence-to-Sequence Multi-Task Model](https://aclanthology.org/D19-1150/) (Song & Park, EMNLP-IJCNLP 2019)

• Korean Morphological Analysis with Tied Sequence-to-Sequence Multi-Task Model (Song & Park, EMNLP-IJCNLP 2019)

ACL

• Hyun-Je Song and Seong-Bae Park. 2019. Korean Morphological Analysis with Tied Sequence-to-Sequence Multi-Task Model. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1436–1441, Hong Kong, China. Association for Computational Linguistics.