Papers › Rethinking Self-Attention: Towards Interpretability in Neural Parsing
Rethinking Self-Attention: Towards Interpretability in Neural Parsing
Khalil Mrini, Franck Dernoncourt, Quan Tran, Trung Bui, Walter Chang, Ndapa Nakashole
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Constituency Parsing | Penn Treebank | Label Attention Layer + HPSG + XLNet | F1 score | 96.38 | #3 of 27 | Archive leaderboard | report |
| Dependency Parsing | Penn Treebank | Label Attention Layer + HPSG + XLNet | LAS | 96.26 | #1 of 22 | Archive leaderboard | report |
| Dependency Parsing | Penn Treebank | Label Attention Layer + HPSG + XLNet | POS | 97.3 | #1 of 22 | Archive leaderboard | report |
| Dependency Parsing | Penn Treebank | Label Attention Layer + HPSG + XLNet | UAS | 97.42 | #1 of 22 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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