Fast End-to-end Coreference Resolution for Korean
Cheoneum Park, Jamin Shin, Sungjoon Park, JoonHo Lim, Changki Lee
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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.