Joint Bi-Affine Parsing and Semantic Role Labeling

被引：0

作者：

Shi, Peng ^{[1
]}

Zhang, Yue ^{[2
]}

机构：

[1] Univ Waterloo, David R Cheriton Sch Comp Sci, Waterloo, ON, Canada

[2] Singapore Univ Technol & Design, Informat Syst Technol & Design Pillar, Singapore, Singapore

来源：

2017 INTERNATIONAL CONFERENCE ON ASIAN LANGUAGE PROCESSING (IALP) | 2017年

关键词：

Parsing; SRL; Joint Learning;

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：

We propose a simple encoder-decoder model for joint learning of dependency parsing and semantic role labeling (SRL). Experiments on CoNLL-2009 datasets show that our model is competitive with the state-of-the-art ensemble model on SRL task and significantly outperforms state-of-the-art joint models on joint evaluation metrics. Results show that with the implicit encoding, the syntax information can further improve a state-of-the-art semantic role labeler.

引用

页码：338 / 341

页数：4

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