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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.findings-emnlp.102.pdf
- Code
- bcmi220/hosrl