DSC IIT-ISM at SemEval-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion Analysis

Pradyumna Gupta, Himanshu Gupta, Aman Sinha


Abstract
Memes have become an ubiquitous social media entity and the processing and analysis of such multimodal data is currently an active area of research. This paper presents our work on the Memotion Analysis shared task of SemEval 2020, which involves the sentiment and humor analysis of memes. We propose a system which uses different bimodal fusion techniques to leverage the inter-modal dependency for sentiment and humor classification tasks. Out of all our experiments, the best system improved the baseline with macro F1 scores of 0.357 on Sentiment Classification (Task A), 0.510 on Humor Classification (Task B) and 0.312 on Scales of Semantic Classes (Task C).
Anthology ID:
2020.semeval-1.111
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
876–884
Language:
URL:
https://aclanthology.org/2020.semeval-1.111
DOI:
10.18653/v1/2020.semeval-1.111
Bibkey:
Cite (ACL):
Pradyumna Gupta, Himanshu Gupta, and Aman Sinha. 2020. DSC IIT-ISM at SemEval-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion Analysis. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 876–884, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
DSC IIT-ISM at SemEval-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion Analysis (Gupta et al., SemEval 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/2020.semeval-1.111.pdf
Code
 dsciitism/SemEval-2020-Task-8
Data
ImageNet