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

arXiv: https://arxiv.org/abs/2302.12794 · HTML: https://mlsanity.com/paper/2302.12794/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 9 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021)
- [Quantifying Intimacy in Language](https://mlsanity.com/paper/2011.03020/) (2020)
- [SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis](https://mlsanity.com/paper/2210.01108/) (2022)

## Abstract (arXiv)

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 urlhttps://github.com/isegura/hulatintimacy.