PoS tags, once taken for granted as a useful resource for syntactic parsing,
have become more situational with the popularization of deep learning. Recent
work on the impact of PoS tags on graph- and transition-based parsers suggests
that they are only useful when tagging accuracy is prohibitively high, or in
low-resource scenarios. However, such an analysis is lacking for the emerging
sequence labeling parsing paradigm, where it is especially relevant as some
models explicitly use PoS tags for encoding and decoding. We undertake a study
and uncover some trends. Among them, PoS tags are generally more useful for
sequence labeling parsers than for other paradigms, but the impact of their
accuracy is highly encoding-dependent, with the PoS-based head-selection
encoding being best only when both tagging accuracy and resource availability
are high.

研究表明，在深度学习背景下，词性标注对于语法分析的作用有限，唯有当标注准确率非常高或资源非常匮乏时才有所帮助；而对于序列标注分析模型而言，词性标注的影响取决于编码方式等因素，其中基于词性的头选择编码方式仅在标注准确率和资源可用性均很高时表现最佳。