Testing pre-trained Transformer models for Lithuanian news clustering

• URL: http://arxiv.org/abs/2004.03461v1
• Date: Fri, 3 Apr 2020 14:41:54 GMT
• Title: Testing pre-trained Transformer models for Lithuanian news clustering
• Authors: Lukas Stankevi\v{c}ius and Mantas Luko\v{s}evi\v{c}ius
• Abstract summary: Non-English languages could not leverage such new opportunities with the English text pre-trained models. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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