Linear Transformations for Cross-lingual Sentiment Analysis

Pavel Přibáň and Jakub Šmíd and Pavel Král
TSD (2022)



. 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.



@inproceedings{pvribavn2022linear, title={Linear Transformations for Cross-lingual Sentiment Analysis}, author={P{\v{r}}ib{\'a}{\v{n}}, Pavel and {\v{S}}m{\'\i}d, Jakub and Mi{\v{s}}tera, Adam and Kr{\'a}l, Pavel}, booktitle={International Conference on Text, Speech, and Dialogue}, pages={125--137}, year={2022}, organization={Springer} }
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