ODA_SRIB at SemEval-2023 Task 9: A Multimodal Approach for Improved Intimacy Analysis

Priyanshu Kumar, Amit Kumar, Jiban Prakash, Prabhat Lamba, Irfan Abdul


Abstract
We experiment with XLM-Twitter and XLM-RoBERTa models to predict the intimacy scores in Tweets i.e. the extent to which a Tweet contains intimate content. We propose a Transformer-TabNet based multimodal architecture using text data and statistical features from the text, which performs better than the vanilla Transformer based model. We further experiment with Adversarial Weight Perturbation to make our models generalized and robust. The ensemble of four of our best models achieve an over-all Pearson Coefficient of 0.5893 on the test dataset.
Anthology ID:
2023.semeval-1.233
Volume:
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1676–1680
Language:
URL:
https://aclanthology.org/2023.semeval-1.233
DOI:
10.18653/v1/2023.semeval-1.233
Bibkey:
Cite (ACL):
Priyanshu Kumar, Amit Kumar, Jiban Prakash, Prabhat Lamba, and Irfan Abdul. 2023. ODA_SRIB at SemEval-2023 Task 9: A Multimodal Approach for Improved Intimacy Analysis. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1676–1680, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
ODA_SRIB at SemEval-2023 Task 9: A Multimodal Approach for Improved Intimacy Analysis (Kumar et al., SemEval 2023)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/2023.semeval-1.233.pdf