In this work, we use a span-based approach for Vietnamese constituency parsing. Our method follows the self-attention encoder architecture and a chart decoder using a CKY-style inference algorithm. We present analyses of the experiment results of the comparison of our empirical method using pre-training models XLM-Roberta and PhoBERT on both Vietnamese datasets VietTreebank and NIIVTB1. The results show that our model with XLM-Roberta archived the significantly F1-score better than other pre-training models, VietTreebank at 81.19% and NIIVTB1 at 85.70%.
Cite
@article{arxiv.2010.09623,
title = {An Empirical Study for Vietnamese Constituency Parsing with Pre-training},
author = {Tuan-Vi Tran and Xuan-Thien Pham and Duc-Vu Nguyen and Kiet Van Nguyen and Ngan Luu-Thuy Nguyen},
journal= {arXiv preprint arXiv:2010.09623},
year = {2020}
}