SRL4ESemantic Role Labeling for Emotions: A Unified Evaluation Framework

Cesare Campagnano, Simone Conia, Roberto Navigli


Abstract
In the field of sentiment analysis, several studies have highlighted that a single sentence may express multiple, sometimes contrasting, sentiments and emotions, each with its own experiencer, target and/or cause. To this end, over the past few years researchers have started to collect and annotate data manually, in order to investigate the capabilities of automatic systems not only to distinguish between emotions, but also to capture their semantic constituents. However, currently available gold datasets are heterogeneous in size, domain, format, splits, emotion categories and role labels, making comparisons across different works difficult and hampering progress in the area. In this paper, we tackle this issue and present a unified evaluation framework focused on Semantic Role Labeling for Emotions (SRL4E), in which we unify several datasets tagged with emotions and semantic roles by using a common labeling scheme. We use SRL4E as a benchmark to evaluate how modern pretrained language models perform and analyze where we currently stand in this task, hoping to provide the tools to facilitate studies in this complex area.
Anthology ID:
2022.acl-long.314
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:
4586–4601
Language:
URL:
https://aclanthology.org/2022.acl-long.314
DOI:
10.18653/v1/2022.acl-long.314
Bibkey:
Cite (ACL):
Cesare Campagnano, Simone Conia, and Roberto Navigli. 2022. SRL4E – Semantic Role Labeling for Emotions: A Unified Evaluation Framework. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4586–4601, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
SRL4E – Semantic Role Labeling for Emotions: A Unified Evaluation Framework (Campagnano et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.314.pdf
Code
 sapienzanlp/srl4e