Rethinking Self-attention: Towards Interpretability In Neural Parsing
Khalil Mrini, Franck Dernoncourt, Quan Tran, Trung Bui, Walter Chang, Ndapa Nakashole . Findings of the Association for Computational Linguistics: EMNLP 2020 2020 – 110 citations
[Paper]
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.