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A Stack-Pointer Network (StackPtr) parser is a pointer network with an internal stack on the decoder. Several studies use the StackPtr as the backbone of a dependency parser because it can traverse a parse tree depth-first without backtracking and can handle high-order parsing information easily thanks to the internal stack. The parser can use information from previously derived subtrees stored in the internal stack upon selecting a child node. In this work, we introduce a new StackPtr parser with Graph Attention Networks (GATs) that can encode a previously derived subtree. We evaluated our proposed parser on the Sejong and Everyone&#x2019;s corpora for Korean and on the Penn Treebank and Universal Dependency corpora for English. In addition, we analyzed and compared our proposed parser with other variants of the StackPtr parser, examining the syntactic information that each parser can reference at every decoding step. We found that Korean parse trees tend to have more consecutive immediate single-child nodes than English parse trees. The proposed StackPtr parser with GATs performed best on almost all metrics for Korean because it can utilize more context to analyze these parse trees by grasping Korean syntactic factors than any other variants. However, for English, no particular variant of the StackPtr parser outperforms the others.

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Syntactic Factors Associated With Performance of Dependency Parsers Using Stack-Pointer Network and Graph Attention Networks Between English and Korean