Better heads do not guarantee better binarized constituency parsing
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Abstract (arXiv)
We revisit punctuation-aware tree binarization for constituency parsing and ask whether dependency-induced headedness improves binary parser supervision. Although learned heads substantially outperform rule-based heads in intrinsic head prediction, they do not yield consistent parsing gains after debinarization. In particular, punctuation-conditioned evaluation shows that learned headedness underperforms rule-based binarization in macro-average punctuation-sensitive F1, despite a small overall gain on CTB. Similar instability appears under cross-treebank transfer. These results suggest that ycclinguistically grounded headedness is not necessarily parser-optimal when used as a binarization control signal. The paper presents a negative result: better head prediction does not imply better punctuation-sensitive constituency parsing.
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References · 3
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2018Constituency Parsing with a Self-Attentive Encodercited by 196
- 2018Multilingual Constituency Parsing with Self-Attention and Pre-Trainingcited by 117
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