@article{M5EE34E3E,
title = "Korean Dependency Parsing Using Stack-Pointer Networksand Subtree Information",
journal = "KIPS Transactions on Software and Data Engineering",
year = "2021",
issn = "2287-5905",
doi = "https://doi.org/10.3745/KTSDE.2021.10.6.235",
author = "Yong-Seok Choi/Kong Joo Lee",
keywords = "Stack-Pointer Networks, Korean Dependency Parser, Graph Attention Networks, Subtree, Pre-trained Word RepresentationModel, Everyone’s Corpus",
abstract = "In this work, we develop a Korean dependency parser based on a stack-pointer network that consists of a pointer network and an
internal stack. The parser has an encoder and decoder and builds a dependency tree for an input sentence in a depth-first manner. The
encoder of the parser encodes an input sentence, and the decoder selects a child for the word at the top of the stack at each step. Since
the parser has the internal stack where a search path is stored, the parser can utilize information of previously derived subtrees when
selecting a child node. Previous studies used only a grandparent and the most recently visited sibling without considering a subtree structure.
In this paper, we introduce graph attention networks that can represent a previously derived subtree. Then we modify our parser based
on the stack-pointer network to utilize subtree information produced by the graph attention networks. After training the dependency parser
using Sejong and Everyone’s corpus, we evaluate the parser’s performance. Experimental results show that the proposed parser achieves
better performance than the previous approaches at sentence-level accuracies when adopting 2-depth graph attention networks."
}