Cross-lingual transfer of sentiment classifiers


  • Marko Robnik-Šikonja University of Ljubljana, Faculty of Computer and Information Science, Slovenia
  • Kristjan Reba University of Ljubljana, Faculty of Computer and Information Science, Slovenia
  • Igor Mozetič Jožef Stefan Institute, Ljubljana, Slovenia



natural language processing, machine learning, text embeddings, sentiment analysis, BERT models


Word embeddings represent words in a numeric space so that semantic relations between words are represented as distances and directions in the vector space. Cross-lingual word embeddings transform vector spaces of different languages so that similar words are aligned. This is done by mapping one language’s vector space to the vector space of another language or by construction of a joint vector space for multiple languages. Cross-lingual embeddings can be used to transfer machine learning models between languages, thereby compensating for insufficient data in less-resourced languages. We use cross-lingual word embeddings to transfer machine learning prediction models for Twitter sentiment between 13 languages. We focus on two transfer mechanisms that recently show superior transfer performance. The first mechanism uses the trained models whose input is the joint numerical space for many languages as implemented in the LASER library. The second mechanism uses large pretrained multilingual BERT language models. Our experiments show that the transfer of models between similar languages is sensible, even with no target language data. The performance of cross-lingual models obtained with the multilingual BERT and LASER library is comparable, and the differences are language-dependent. The transfer with CroSloEngual BERT, pretrained on only three languages, is superior on these and some closely related languages.


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01.07.2021 — Updated on 06.07.2021


How to Cite

Robnik-Šikonja, M., Reba, K., & Mozetič, I. (2021). Cross-lingual transfer of sentiment classifiers. Slovenščina 2.0: Empirical, Applied and Interdisciplinary Research, 9(1), 1–25. (Original work published July 1, 2021)