Acta Scientiarum Naturalium Universitatis Pekinensis ›› 2020, Vol. 56 ›› Issue (1): 89-96.DOI: 10.13209/j.0479-8023.2019.099

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Syntax-Enhanced UCCA Semantic Parsing

JIANG Wei, LI Zhenghua, ZHANG Min   

  1. School of Computer Science and Technology, Soochow University, Suzhou 215006
  • Received:2019-05-22 Revised:2019-09-19 Online:2020-01-20 Published:2020-01-20
  • Contact: LI Zhenghua, E-mail: zhli13(at)


蒋炜, 李正华, 张民   

  1. 苏州大学计算机科学与技术学院, 苏州 215006
  • 通讯作者: 李正华, E-mail: zhli13(at)
  • 基金资助:
    国家自然科学基金(61876116, 61525205)和江苏高校优势学科建设工程项目资助


Considering the close correlation between syntactic and semantic structures, this paper attempts to add syntactic information into the universal conceptual cognitive annotation (UCCA) semantic parsing model to enhance the performance of semantic parsing. Based on the state-of-the-art graph-based UCCA semantic parser, we propose and compare four different approaches for incorporating syntactic information. Experiments are conducted on the English benchmark dataset for the semantic parsing shared task of the SemEval-2019 conference. The results on both the in-domain and out-domain evaluation data show that syntax-enhanced methods can achieve significant improvements of UCCA parsing. After utilizing BERT, syntactic information is still beneficial to some extent.

Key words: semantic parsing, UCCA, syntactic parsing


考虑到句法结构与语义结构之间的紧密联系, 尝试将句法信息融入UCCA语义分析模型中来增强语义分析的性能。基于目前性能最好的基于图的 UCCA语义分析模型, 提出并比较4种不同的融入依存句法信息的方法。采用SemEval-2019国际评测语义分析任务的英文数据集进行实验, 在本领域和跨领域两个数据集上的结果均表明, 句法增强的方法能够给显著地提高UCCA分析性能。引入BERT特征后, 句法信息仍然可以提供一定的帮助。

关键词: 语义分析, UCCA, 句法分析