ODA_SRIB at SemEval-2023 Task 9: A Multimodal Approach for Improved Intimacy Analysis
Priyanshu Kumar, Amit Kumar, Jiban Prakash, Prabhat Lamba, Irfan Abdul
The 17th International Workshop on Semantic Evaluation (SemEval-2023) Task 9: multilingual tweet intimacy analysis Paper
TLDR:
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
You can open the
#paper-SemEval_256
channel in a separate window.
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.