In this paper, we aimed to develop a neural parser for Vietnamese based on simplified Head-Driven Phrase Structure Grammar (HPSG). The existing corpora, VietTreebank and VnDT, had around 15% of constituency and dependency tree pairs that did not adhere to simplified HPSG rules. To attempt to address the issue of the corpora not adhering to simplified HPSG rules, we randomly permuted samples from the training and development sets to make them compliant with simplified HPSG. We then modified the first simplified HPSG Neural Parser for the Penn Treebank by replacing it with the PhoBERT or XLM-RoBERTa models, which can encode Vietnamese texts. We conducted experiments on our modified VietTreebank and VnDT corpora. Our extensive experiments showed that the simplified HPSG Neural Parser achieved a new state-of-the-art F-score of 82% for constituency parsing when using the same predicted part-of-speech (POS) tags as the self-attentive constituency parser. Additionally, it outperformed previous studies in dependency parsing with a higher Unlabeled Attachment Score (UAS). However, our parser obtained lower Labeled Attachment Score (LAS) scores likely due to our focus on arc permutation without changing the original labels, as we did not consult with a linguistic expert. Lastly, the research findings of this paper suggest that simplified HPSG should be given more attention to linguistic expert when developing treebanks for Vietnamese natural language processing.

翻译：本文旨在基于简化中心词驱动短语结构语法（HPSG）开发越南语神经解析器。现有语料库VietTreebank和VnDT中约有15%的句法成分树与依存树对未遵循简化HPSG规则。为解决语料库不遵循简化HPSG规则的问题，我们通过随机置换训练集和开发集中的样本使其符合简化HPSG规范。随后，我们针对宾州树库的首个简化HPSG神经解析器进行改进，将其替换为能够编码越南语文本的PhoBERT或XLM-RoBERTa模型。我们在改进后的VietTreebank和VnDT语料库上进行了实验。大量实验表明，在使用与自注意力成分解析器相同的预测词性标注时，简化HPSG神经解析器在成分解析任务中取得了82%的F值，创造了新的最优性能记录。此外，该解析器在依存解析任务中以更高的未标记依存正确率（UAS）超越了先前研究。然而，由于我们在未咨询语言学专家的情况下仅进行依存弧置换而未改变原始标签，解析器的标记依存正确率（LAS）得分较低。最后，本文研究结果表明，在开发越南语自然语言处理树库时，应更加重视简化HPSG与语言学专家的协同工作。