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.- Anthology ID:
- D19-1150
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1436–1441
- Language:
- URL:
- https://aclanthology.org/D19-1150
- DOI:
- 10.18653/v1/D19-1150
- Cite (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.
- Cite (Informal):
- Korean Morphological Analysis with Tied Sequence-to-Sequence Multi-Task Model (Song & Park, EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/D19-1150.pdf