Papers › Ensemble Distillation for Unsupervised Constituency Parsing
Ensemble Distillation for Unsupervised Constituency Parsing
Behzad Shayegh, Yanshuai Cao, Xiaodan Zhu, Jackie C. K. Cheung, Lili Mou
We investigate the unsupervised constituency parsing task, which organizes words and phrases of a sentence into a hierarchical structure without using linguistically annotated data. We observe that existing unsupervised parsers capture differing aspects of parsing structures, which can be leveraged to enhance unsupervised parsing performance. To this end, we propose a notion of "tree averaging," based on which we further propose a novel ensemble method for unsupervised parsing. To improve inference efficiency, we further distill the ensemble knowledge into a student model; such an ensemble-then-distill process is an effective approach to mitigate the over-smoothing problem existing in common multi-teacher distilling methods. Experiments show that our method surpasses all previous approaches, consistently demonstrating its effectiveness and robustness across various runs, with different ensemble components, and under domain-shift conditions.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Constituency Grammar Induction | PTB Diagnostic ECG Database | Ensemble (Generative MBR) | Max F1 (WSJ) | 71.9 | #1 of 24 | Archive leaderboard | report |
| Constituency Grammar Induction | PTB Diagnostic ECG Database | Ensemble (Generative MBR) | Mean F1 (WSJ) | 70.4 | #1 of 24 | Archive leaderboard | report |
| Constituency Grammar Induction | PTB Diagnostic ECG Database | Ensemble (Selective MBR) | Mean F1 (WSJ) | 66.2 | #5 of 24 | 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.
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