@inproceedings{kumar-etal-2023-oda,
title = "{ODA}{\_}{SRIB} at {S}em{E}val-2023 Task 9: A Multimodal Approach for Improved Intimacy Analysis",
author = "Kumar, Priyanshu and
Kumar, Amit and
Prakash, Jiban and
Lamba, Prabhat and
Abdul, Irfan",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.233",
doi = "10.18653/v1/2023.semeval-1.233",
pages = "1676--1680",
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.",
}
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%0 Conference Proceedings
%T ODA_SRIB at SemEval-2023 Task 9: A Multimodal Approach for Improved Intimacy Analysis
%A Kumar, Priyanshu
%A Kumar, Amit
%A Prakash, Jiban
%A Lamba, Prabhat
%A Abdul, Irfan
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F kumar-etal-2023-oda
%X 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.
%R 10.18653/v1/2023.semeval-1.233
%U https://aclanthology.org/2023.semeval-1.233
%U https://doi.org/10.18653/v1/2023.semeval-1.233
%P 1676-1680
Markdown (Informal)
[ODA_SRIB at SemEval-2023 Task 9: A Multimodal Approach for Improved Intimacy Analysis](https://aclanthology.org/2023.semeval-1.233) (Kumar et al., SemEval 2023)
ACL