Fast End-to-end Coreference Resolution for Korean
Cheoneum Park, Jamin Shin, Sungjoon Park, Joonho Lim, Changki Lee
Abstract
Recently, end-to-end neural network-based approaches have shown significant improvements over traditional pipeline-based models in English coreference resolution. However, such advancements came at a cost of computational complexity and recent works have not focused on tackling this problem. Hence, in this paper, to cope with this issue, we propose BERT-SRU-based Pointer Networks that leverages the linguistic property of head-final languages. Applying this model to the Korean coreference resolution, we significantly reduce the coreference linking search space. Combining this with Ensemble Knowledge Distillation, we maintain state-of-the-art performance 66.9% of CoNLL F1 on ETRI test set while achieving 2x speedup (30 doc/sec) in document processing time.- Anthology ID:
- 2020.findings-emnlp.237
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2610–2624
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.237
- DOI:
- 10.18653/v1/2020.findings-emnlp.237
- Cite (ACL):
- Cheoneum Park, Jamin Shin, Sungjoon Park, Joonho Lim, and Changki Lee. 2020. Fast End-to-end Coreference Resolution for Korean. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2610–2624, Online. Association for Computational Linguistics.
- Cite (Informal):
- Fast End-to-end Coreference Resolution for Korean (Park et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.findings-emnlp.237.pdf