Combining Language Models and Linguistic Information to Label Entities in Memes

Pranaydeep Singh, Aaron Maladry, Els Lefever


Abstract
This paper describes the system we developed for the shared task ‘Hero, Villain and Victim: Dissecting harmful memes for Semantic role labelling of entities’ organised in the framework of the Second Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situation (Constraint 2022). We present an ensemble approach combining transformer-based models and linguistic information, such as the presence of irony and implicit sentiment associated to the target named entities. The ensemble system obtains promising classification scores, resulting in a third place finish in the competition.
Anthology ID:
2022.constraint-1.5
Volume:
Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Tanmoy Chakraborty, Md. Shad Akhtar, Kai Shu, H. Russell Bernard, Maria Liakata, Preslav Nakov, Aseem Srivastava
Venue:
CONSTRAINT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
35–42
Language:
URL:
https://aclanthology.org/2022.constraint-1.5
DOI:
10.18653/v1/2022.constraint-1.5
Bibkey:
Cite (ACL):
Pranaydeep Singh, Aaron Maladry, and Els Lefever. 2022. Combining Language Models and Linguistic Information to Label Entities in Memes. In Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations, pages 35–42, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Combining Language Models and Linguistic Information to Label Entities in Memes (Singh et al., CONSTRAINT 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.constraint-1.5.pdf
Video:
 https://aclanthology.org/2022.constraint-1.5.mp4