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arXiv · 2004.03461

Testing pre-trained Transformer models for Lithuanian news clustering

Lukas Stankevičius·Mantas Lukoševičius
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Abstract

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.

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BibTeXRIS

Lukas Stankevičius, Mantas Lukoševičius. 2020-04-03. Testing pre-trained Transformer models for Lithuanian news clustering. https://arxiv.org/abs/2004.03461

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