Rethinking Self-Attention: Towards Interpretability in Neural Parsing
Abstract
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.
Cite
@article{arxiv.1911.03875,
title = {Rethinking Self-Attention: Towards Interpretability in Neural Parsing},
author = {Khalil Mrini and Franck Dernoncourt and Quan Tran and Trung Bui and Walter Chang and Ndapa Nakashole},
journal= {arXiv preprint arXiv:1911.03875},
year = {2020}
}
Comments
EMNLP 2020