HULAT at SemEval-2023 Task 9: Data augmentation for pre-trained transformers applied to Multilingual Tweet Intimacy Analysis

Isabel Segura-Bedmar
2/24/2023
cs.CLcs.AIcs.LG

Official code: isegura/hulat_intimacy (0 stars, last commit 2025-05-27).

Abstract

This paper describes our participation in SemEval-2023 Task 9, Intimacy Analysis of Multilingual Tweets. We fine-tune some of the most popular transformer models with the training dataset and synthetic data generated by different data augmentation techniques. During the development phase, our best results were obtained by using XLM-T. Data augmentation techniques provide a very slight improvement in the results. Our system ranked in the 27th position out of the 45 participating systems. Despite its modest results, our system shows promising results in languages such as Portuguese, English, and Dutch. All our code is available in the repository \url{https://github.com/isegura/hulat_intimacy}.

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isegura/hulat_intimacyOfficial100%

This repository contains all the code of our participation (HULAT) at emEval-2023 Task 9: Data augmentation for pre-trained transformers applied to Multilingual Tweet Intimacy Analysis

01Jupyter NotebookFeb 17, 20231 years ago

Cite this paper

@article{segurabedmar2023hulat,
  title  = {HULAT at SemEval-2023 Task 9: Data augmentation for pre-trained transformers applied to Multilingual Tweet Intimacy Analysis},
  author = {Isabel Segura-Bedmar},
  year   = {2023},
  eprint = {2302.12794},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.12794v1}
}

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