Cross-lingual deep neural transfer learning in sentiment analysis
Kamil Kanclerz, Piotr Miłkowski, Jan Kocoń
Abstract
In this article, we present a novel technique for the use of language-agnostic sentence representations to adapt the model trained on texts in Polish (as a low-resource language) to recognize polarity in texts in other (high-resource) languages. The first model focuses on the creation of a language-agnostic representation of each sentence. The second one aims to predict the sentiment of the text based on these sentence representations. Besides models evaluation on PolEmo 1.0 Sentiment Corpus, we also conduct a proof of concept for using a deep neural network model trained only on language-agnostic embeddings of texts in Polish to predict the sentiment of the texts in MultiEmo-Test 1.0 Sentiment Corpus, containing PolEmo 1.0 test datasets translated into eight different languages: Dutch, English, French, German, Italian, Portuguese, Russian and Spanish. Both corpora are publicly available under a Creative Commons copyright license.