Character-Level Syntax Infusion in Pre-Trained Models for Chinese Semantic Role Labeling

2021 ◽  
Author(s):  
Yuxuan Wang ◽  
Zhilin Lei ◽  
Wanxiang Che
Keyword(s):  
Semantic Role ◽  
Semantic Role Labeling

Chinese Semantic Role Labeling Based on Feature Combination

2011 ◽  
Vol 22 (2) ◽  
pp. 222-232 ◽  
Author(s):  
Shi-Qi LI ◽  
Tie-Jun ZHAO ◽  
Han-Jing LI ◽  
Peng-Yuan LIU ◽  
Shui LIU
Keyword(s):  
Semantic Role ◽  
Feature Combination ◽  
Semantic Role Labeling

Tree kernel-based semantic role labeling with enriched parse tree structure

2011 ◽  
Vol 47 (3) ◽  
pp. 349-362 ◽  
Author(s):  
GuoDong Zhou ◽  
Junhui Li ◽  
Jianxi Fan ◽  
Qiaoming Zhu
Keyword(s):  
Tree Structure ◽  
Parse Tree ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Tree Kernel

scholarly journals Syntax Role for Neural Semantic Role Labeling

2021 ◽  
pp. 1-48
Author(s):  
Zuchao Li ◽  
Hai Zhao ◽  
Shexia He ◽  
Jiaxun Cai
Keyword(s):  
Argument Structure ◽  
Large Scale ◽  
Language Models ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Empirical Survey ◽  
Learning Framework ◽  
Syntactic Information ◽  
Feature Based ◽  
Predicate Argument Structure

Abstract Semantic role labeling (SRL) is dedicated to recognizing the semantic predicate-argument structure of a sentence. Previous studies in terms of traditional models have shown syntactic information can make remarkable contributions to SRL performance; however, the necessity of syntactic information was challenged by a few recent neural SRL studies that demonstrate impressive performance without syntactic backbones and suggest that syntax information becomes much less important for neural semantic role labeling, especially when paired with recent deep neural network and large-scale pre-trained language models. Despite this notion, the neural SRL field still lacks a systematic and full investigation on the relevance of syntactic information in SRL, for both dependency and both monolingual and multilingual settings. This paper intends to quantify the importance of syntactic information for neural SRL in the deep learning framework. We introduce three typical SRL frameworks (baselines), sequence-based, tree-based, and graph-based, which are accompanied by two categories of exploiting syntactic information: syntax pruningbased and syntax feature-based. Experiments are conducted on the CoNLL-2005, 2009, and 2012 benchmarks for all languages available, and results show that neural SRL models can still benefit from syntactic information under certain conditions. Furthermore, we show the quantitative significance of syntax to neural SRL models together with a thorough empirical survey using existing models.

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