Biaffine Dependency and Semantic Graph Parsing for Enhanced Universal Dependencies

被引:0
|
作者
Attardi, Giuseppe [1 ]
Sartiano, Daniele [1 ]
Simi, Maria [1 ]
机构
[1] Univ Pisa, Dipartmento Informat, Pisa, Italy
来源
IWPT 2021: THE 17TH INTERNATIONAL CONFERENCE ON PARSING TECHNOLOGIES: PROCEEDINGS OF THE CONFERENCE (INCLUDING THE IWPT 2021 SHARED TASK) | 2021年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents the system used in our submission to the IWPT 2021 Shared Task. This year the official evaluation metrics was ELAS, therefore dependency parsing might have been avoided as well as other pipeline stages like POS tagging and lemmatization. We nevertheless chose to deploy a combination of a dependency parser and a graph parser. The dependency parser is a biaffine parser, that uses transformers for representing input sentences, with no other feature. The graph parser is a semantic parser that exploits a similar architecture except for using a sigmoid crossentropy loss function to return multiple values for the predicted arcs. The final output is obtained by merging the output of the two parsers. The dependency parser achieves top or close to top LAS performance with respect to other systems that report results on such metrics, except on low resource languages (Tamil, Estonian, Latvian).
引用
收藏
页码:184 / 188
页数:5
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