High-order Semantic Role Labeling

Zuchao Li, Hai Zhao, Rui Wang, Kevin Parnow


Abstract
Semantic role labeling is primarily used to identify predicates, arguments, and their semantic relationships. Due to the limitations of modeling methods and the conditions of pre-identified predicates, previous work has focused on the relationships between predicates and arguments and the correlations between arguments at most, while the correlations between predicates have been neglected for a long time. High-order features and structure learning were very common in modeling such correlations before the neural network era. In this paper, we introduce a high-order graph structure for the neural semantic role labeling model, which enables the model to explicitly consider not only the isolated predicate-argument pairs but also the interaction between the predicate-argument pairs. Experimental results on 7 languages of the CoNLL-2009 benchmark show that the high-order structural learning techniques are beneficial to the strong performing SRL models and further boost our baseline to achieve new state-of-the-art results.
Anthology ID:
2020.findings-emnlp.102
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1134–1151
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.102
DOI:
10.18653/v1/2020.findings-emnlp.102
Bibkey:
Cite (ACL):
Zuchao Li, Hai Zhao, Rui Wang, and Kevin Parnow. 2020. High-order Semantic Role Labeling. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1134–1151, Online. Association for Computational Linguistics.
Cite (Informal):
High-order Semantic Role Labeling (Li et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.102.pdf
Optional supplementary material:
 2020.findings-emnlp.102.OptionalSupplementaryMaterial.zip
Code
 bcmi220/hosrl