# Testing pre-trained Transformer models for Lithuanian news clustering

arXiv: https://arxiv.org/abs/2004.03461 · HTML: https://mlsanity.com/paper/2004.03461/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [Russian News Clustering and Headline Selection Shared Task](https://mlsanity.com/paper/2105.00981/) (2021)

## Abstract (arXiv)

A recent introduction of Transformer deep learning architecture made breakthroughs in various natural language processing tasks. However, non-English languages could not leverage such new opportunities with the English text pre-trained models. This changed with research focusing on multilingual models, where less-spoken languages are the main beneficiaries. We compare pre-trained multilingual BERT, XLM-R, and older learned text representation methods as encodings for the task of Lithuanian news clustering. Our results indicate that publicly available pre-trained multilingual Transformer models can be fine-tuned to surpass word vectors but still score much lower than specially trained doc2vec embeddings.