高阶联合成分句法与依存句法分析
High-order Joint Constituency and Dependency Parsing
AI总结：
本工作提出一种高阶联合成分与依存句法分析方法，通过在训练阶段引入联合建模、采用$O(n^4)$高效解码算法及高阶评分组件，显著提升了多语言场景下句法树的完全匹配率。
摘要：
本工作重新探讨了联合解析成分句法树和依存句法树的主题，即为输入句子同时生成兼容的成分句法树和依存句法树。鉴于这两类树在表示句法时具有互补性，这一研究颇具吸引力。Zhou和Zhao（2019）的早期工作仅在推理阶段进行联合解析。他们在多任务学习框架下（即一个共享编码器和两个独立解码器）训练了两个独立的解析器。他们设计了一种基于动态规划的特定解码算法，时间复杂度为$O(n^5)$，用于寻找最优的兼容树对。与他们的工作相比，我们在三个方面取得了进展：（1）采用了时间复杂度为$O(n^4)$的更高效解码算法；（2）在训练阶段探索联合建模，而非仅在推理阶段；（3）提出高阶评分组件以促进成分与依存之间的交互。我们在七种语言上进行了实验与分析，涵盖资源丰富和资源匮乏两种场景。结果与分析表明，得益于对树兼容性的显式建模，联合建模相比独立建模带来了适度的整体性能提升，并大幅提升了整棵树的完全匹配率。
英文摘要：
This work revisits the topic of jointly parsing constituency and dependency trees, i.e., to produce compatible constituency and dependency trees simultaneously for input sentences, which is attractive considering that the two types of trees are complementary in representing syntax. The original work of Zhou and Zhao (2019) performs joint parsing only at the inference phase. They train two separate parsers under the multi-task learning framework (i.e., one shared encoder and two independent decoders). They design an ad-hoc dynamic programming-based decoding algorithm of $O(n^5)$ time complexity for finding optimal compatible tree pairs. Compared to their work, we make progress in three aspects: (1) adopting a much more efficient decoding algorithm of $O(n^4)$ time complexity, (2) exploring joint modeling at the training phase, instead of only at the inference phase, (3) proposing high-order scoring components to promote constituent-dependency interaction. We conduct experiments and analysis on seven languages, covering both rich-resource and low-resource scenarios. Results and analysis show that joint modeling leads to a modest overall performance boost over separate modeling, but substantially improves the complete matching ratio of whole trees, thanks to the explicit modeling of tree compatibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
