Deep Neural Language-agnostic Multi-task Text Classifier.

Many publications prove that the creation of a multiobjective machine learning model is possible and reasonable. Moreover, we can see significant gains in expanding the knowledge domain, increasing prediction quality, and reducing the inference time. New developments in cross-lingual knowledge transfer open up a range of possibilities, particularly in working with low-resource languages. With a motivation to explore the latest subfields of natural language processing and their interactions, we decided to create a multi-task multilingual model for the following text classification tasks: functional style, domain, readability, and sentiment. The paper discusses the effectiveness of particular language-agnostic approaches to Polish and English and the effectiveness and validity of the multi-task model.