This paper deals with cross-lingual sentiment analysis in Czech, English and French languages. We perform zero-shot cross-lingual classification using five linear transformations combined with LSTM and CNN based classifiers. We compare the performance of the individual transformations, and in addition, we confront the transformation-based approach with existing state-of-the-art BERT-like models. We show that the pre-trained embeddings from the target domain are crucial to improving the cross-lingual classification results, unlike in the monolingual classification, where the effect is not so distinctive.
CITATION STYLE
Přibáň, P., Šmíd, J., Mištera, A., & Král, P. (2022). Linear Transformations for Cross-lingual Sentiment Analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13502 LNAI, pp. 125–137). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16270-1_11
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