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

Abstract

We experiment with XLM-Twitter and XLMRoBERTa 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.