Automatic dependency parsing of Thai sentences has been underexplored, as
evidenced by the lack of large Thai dependency treebanks with complete
dependency structures and the lack of a published systematic evaluation of
state-of-the-art models, especially transformer-based parsers. In this work, we
address these problems by introducing Thai Universal Dependency Treebank (TUD),
a new largest Thai treebank consisting of 3,627 trees annotated in accordance
with the Universal Dependencies (UD) framework. We then benchmark dependency
parsing models that incorporate pretrained transformers as encoders and train
them on Thai-PUD and our TUD. The evaluation results show that most of our
models can outperform other models reported in previous papers and provide
insight into the optimal choices of components to include in Thai dependency
parsers. The new treebank and every model's full prediction generated in our
experiment are made available on a GitHub repository for further study.

本研究通过引入泰语通用依存树库（TUD）解决了自动依存解析泰语句子的问题，并通过将预训练的 transformer 作为编码器来训练泰语依存解析模型，在评估结果中表明大多数模型能够胜过前期研究中的其他模型，为泰语依存解析器的最佳组件选择提供了见解，并将实验中的新树库和每个模型的完整预测结果在 GitHub 上进行了共享。