@inproceedings{xia-etal-2020-semantic,
title = "Semantic Role Labeling with Heterogeneous Syntactic Knowledge",
author = "Xia, Qingrong and
Wang, Rui and
Li, Zhenghua and
Zhang, Yue and
Zhang, Min",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.266",
doi = "10.18653/v1/2020.coling-main.266",
pages = "2979--2990",
abstract = "Recently, due to the interplay between syntax and semantics, incorporating syntactic knowledge into neural semantic role labeling (SRL) has achieved much attention. Most of the previous syntax-aware SRL works focus on explicitly modeling homogeneous syntactic knowledge over tree outputs. In this work, we propose to encode \textit{heterogeneous} syntactic knowledge for SRL from both explicit and implicit representations. First, we introduce graph convolutional networks to explicitly encode multiple heterogeneous dependency parse trees. Second, we extract the implicit syntactic representations from syntactic parser trained with heterogeneous treebanks. Finally, we inject the two types of heterogeneous syntax-aware representations into the base SRL model as extra inputs. We conduct experiments on two widely-used benchmark datasets, i.e., Chinese Proposition Bank 1.0 and English CoNLL-2005 dataset. Experimental results show that incorporating heterogeneous syntactic knowledge brings significant improvements over strong baselines. We further conduct detailed analysis to gain insights on the usefulness of heterogeneous (vs. homogeneous) syntactic knowledge and the effectiveness of our proposed approaches for modeling such knowledge.",
}
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<abstract>Recently, due to the interplay between syntax and semantics, incorporating syntactic knowledge into neural semantic role labeling (SRL) has achieved much attention. Most of the previous syntax-aware SRL works focus on explicitly modeling homogeneous syntactic knowledge over tree outputs. In this work, we propose to encode heterogeneous syntactic knowledge for SRL from both explicit and implicit representations. First, we introduce graph convolutional networks to explicitly encode multiple heterogeneous dependency parse trees. Second, we extract the implicit syntactic representations from syntactic parser trained with heterogeneous treebanks. Finally, we inject the two types of heterogeneous syntax-aware representations into the base SRL model as extra inputs. We conduct experiments on two widely-used benchmark datasets, i.e., Chinese Proposition Bank 1.0 and English CoNLL-2005 dataset. Experimental results show that incorporating heterogeneous syntactic knowledge brings significant improvements over strong baselines. We further conduct detailed analysis to gain insights on the usefulness of heterogeneous (vs. homogeneous) syntactic knowledge and the effectiveness of our proposed approaches for modeling such knowledge.</abstract>
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%0 Conference Proceedings
%T Semantic Role Labeling with Heterogeneous Syntactic Knowledge
%A Xia, Qingrong
%A Wang, Rui
%A Li, Zhenghua
%A Zhang, Yue
%A Zhang, Min
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F xia-etal-2020-semantic
%X Recently, due to the interplay between syntax and semantics, incorporating syntactic knowledge into neural semantic role labeling (SRL) has achieved much attention. Most of the previous syntax-aware SRL works focus on explicitly modeling homogeneous syntactic knowledge over tree outputs. In this work, we propose to encode heterogeneous syntactic knowledge for SRL from both explicit and implicit representations. First, we introduce graph convolutional networks to explicitly encode multiple heterogeneous dependency parse trees. Second, we extract the implicit syntactic representations from syntactic parser trained with heterogeneous treebanks. Finally, we inject the two types of heterogeneous syntax-aware representations into the base SRL model as extra inputs. We conduct experiments on two widely-used benchmark datasets, i.e., Chinese Proposition Bank 1.0 and English CoNLL-2005 dataset. Experimental results show that incorporating heterogeneous syntactic knowledge brings significant improvements over strong baselines. We further conduct detailed analysis to gain insights on the usefulness of heterogeneous (vs. homogeneous) syntactic knowledge and the effectiveness of our proposed approaches for modeling such knowledge.
%R 10.18653/v1/2020.coling-main.266
%U https://aclanthology.org/2020.coling-main.266
%U https://doi.org/10.18653/v1/2020.coling-main.266
%P 2979-2990
Markdown (Informal)
[Semantic Role Labeling with Heterogeneous Syntactic Knowledge](https://aclanthology.org/2020.coling-main.266) (Xia et al., COLING 2020)
ACL
- Qingrong Xia, Rui Wang, Zhenghua Li, Yue Zhang, and Min Zhang. 2020. Semantic Role Labeling with Heterogeneous Syntactic Knowledge. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2979–2990, Barcelona, Spain (Online). International Committee on Computational Linguistics.