# Text Intimacy Analysis using Ensembles of Multilingual Transformers

arXiv: https://arxiv.org/abs/2312.02590 · HTML: https://mlsanity.com/paper/2312.02590/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (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)

Intimacy estimation of a given text has recently gained importance due to the increase in direct interaction of NLP systems with humans. Intimacy is an important aspect of natural language and has a substantial impact on our everyday communication. Thus the level of intimacy can provide us with deeper insights and richer semantics of conversations. In this paper, we present our work on the SemEval shared task 9 on predicting the level of intimacy for the given text. The dataset consists of tweets in ten languages, out of which only six are available in the training dataset. We conduct several experiments and show that an ensemble of multilingual models along with a language-specific monolingual model has the best performance. We also evaluate other data augmentation methods such as translation and present the results. Lastly, we study the results thoroughly and present some noteworthy insights into this problem.