让模型直接预测句法成分的中心词，提升依存转换精度。
Learning Constituent Headedness
- 将成分中心性作为显式任务学习，利用对齐句法与依存标注监督训练。
- 在英汉语料上达到接近上限的准确率，显著优于传统规则推导方法。
- 提升句法到依存转换的保真度，支持跨语言资源迁移。
中心性是句法分析中的核心组织原则，但句法树库通常未显式标注，多数处理流程依赖柯林斯式规则逐层推导。本文将成分中心性视为显式表征层，基于对齐的句法与依存标注，将其建模为监督预测任务，通过定义每个成分的中心词为其依存跨度的头部来获取监督信号。在对齐的英语和中文数据上，所提模型达到近上限的内在准确率，显著优于柯林斯风格的规则推导方法。预测出的中心词在中心驱动的二元化解析中带来相当的解析精度，表明不同中心选择下的二元训练目标基本等价；同时提升了确定性句法到依存转换的保真度，并可通过简单的标签映射接口实现跨资源与跨语言迁移。
原文摘要 · Abstract (English)
Headedness is widely used as an organizing device in syntactic analysis, yet constituency treebanks rarely encode it explicitly and most processing pipelines recover it procedurally via percolation rules. We treat this notion of constituent headedness as an explicit representational layer and learn it as a supervised prediction task over aligned constituency and dependency annotations, inducing supervision by defining each constituent head as the dependency span head. On aligned English and Chinese data, the resulting models achieve near-ceiling intrinsic accuracy and substantially outperform Collins-style rule-based percolation. Predicted heads yield comparable parsing accuracy under head-driven binarization, consistent with the induced binary training targets being largely equivalent across head choices, while increasing the fidelity of deterministic constituency-to-dependency conversion and transferring across resources and languages under simple label-mapping interfaces.
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