Structured Tuning for Semantic Role Labeling

Tao Li, Parth Anand Jawale, Martha Palmer, Vivek Srikumar


Abstract
Recent neural network-driven semantic role labeling (SRL) systems have shown impressive improvements in F1 scores. These improvements are due to expressive input representations, which, at least at the surface, are orthogonal to knowledge-rich constrained decoding mechanisms that helped linear SRL models. Introducing the benefits of structure to inform neural models presents a methodological challenge. In this paper, we present a structured tuning framework to improve models using softened constraints only at training time. Our framework leverages the expressiveness of neural networks and provides supervision with structured loss components. We start with a strong baseline (RoBERTa) to validate the impact of our approach, and show that our framework outperforms the baseline by learning to comply with declarative constraints. Additionally, our experiments with smaller training sizes show that we can achieve consistent improvements under low-resource scenarios.
Anthology ID:
2020.acl-main.744
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8402–8412
Language:
URL:
https://aclanthology.org/2020.acl-main.744
DOI:
10.18653/v1/2020.acl-main.744
Bibkey:
Cite (ACL):
Tao Li, Parth Anand Jawale, Martha Palmer, and Vivek Srikumar. 2020. Structured Tuning for Semantic Role Labeling. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8402–8412, Online. Association for Computational Linguistics.
Cite (Informal):
Structured Tuning for Semantic Role Labeling (Li et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.744.pdf
Video:
 http://slideslive.com/38929153
Code
 utahnlp/structured_tuning_srl