Attention mechanisms have improved the performance of NLP tasks while
allowing models to remain explainable. Self-attention is currently widely used,
however interpretability is difficult due to the numerous attention
distributions. Recent work has shown that model representations can benefit
from label-specific information, while facilitating interpretation of
predictions. We introduce the Label Attention Layer: a new form of
self-attention where attention heads represent labels. We test our novel layer
by running constituency and dependency parsing experiments and show our new
model obtains new state-of-the-art results for both tasks on both the Penn
Treebank (PTB) and Chinese Treebank. Additionally, our model requires fewer
self-attention layers compared to existing work. Finally, we find that the
Label Attention heads learn relations between syntactic categories and show
pathways to analyze errors.

本研究提出了标签关注层，一种新的自注意形式，其中关注头表示标签。运行实验并在 Penn Treebank（PTB）和中文 Treebank 上展示出其在词法句法分析方面的最新成果，标签关注层在该实验中表现出了更好的性能，相比现有工作需要较少的自我注意层数。最后，我们发现标签注意头学习了语法类别之间的关系，并显示了分析错误的路径。