Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models

Junjie Chen, Xiangheng He, Yusuke Miyao


Abstract
In this paper, we propose a mixture model-based end-to-end method to model the syntactic-semantic dependency correlation in Semantic Role Labeling (SRL). Semantic dependencies in SRL are modeled as a distribution over semantic dependency labels conditioned on a predicate and an argument word.The semantic label distribution varies depending on Shortest Syntactic Dependency Path (SSDP) hop patterns.We target the variation of semantic label distributions using a mixture model, separately estimating semantic label distributions for different hop patterns and probabilistically clustering hop patterns with similar semantic label distributions.Experiments show that the proposed method successfully learns a cluster assignment reflecting the variation of semantic label distributions.Modeling the variation improves performance in predicting short distance semantic dependencies, in addition to the improvement on long distance semantic dependencies that previous syntax-aware methods have achieved.The proposed method achieves a small but statistically significant improvement over baseline methods in English, German, and Spanish and obtains competitive performance with state-of-the-art methods in English.
Anthology ID:
2022.acl-long.548
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7959–7969
Language:
URL:
https://aclanthology.org/2022.acl-long.548
DOI:
10.18653/v1/2022.acl-long.548
Bibkey:
Cite (ACL):
Junjie Chen, Xiangheng He, and Yusuke Miyao. 2022. Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7959–7969, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models (Chen et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.548.pdf
Software:
 2022.acl-long.548.software.zip
Code
 christomartin/syn-sem_dependency_correlation_mixture_model