An Empirical Study for Vietnamese Constituency Parsing with Pre-training

Abstract

Constituency parsing is an important task that gets more attention in natural language processing. 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-R and PhoBERT on both Vietnamese datasets VietTreebank and NIIVTB1. The results show that our model with XLM-R archived the significantly F1-score better than other pre-training models, VietTreebank at 81.19% and NIIVTB1 at 85.70%.