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The growth of social media worldwide has brought social benefits and challenges. One problem we highlight is the proliferation of hate speech on social media. We propose a novel method for detecting hate speech in texts using Cross-Lingual Learning. Our approach uses transfer learning from Pre-Trained Language Models (PTLM) with large corpora available to solve problems in languages with fewer resources for the specific task. The proposed methodology comprises four stages: corpora acquisition, the PTLM definition, training strategies, and evaluation. We carried out experiments using Pre-Trained Language Models in English, Italian, and Portuguese (BERT and XLM-R) to verify which best suited the proposed method. We used corpora in English (WH) and Italian (Evalita 2018) as the source language and the OffComBr-2 corpus in Portuguese (the target language). The results of the experiments showed that the proposed methodology is promising: for the OffComBr-2 corpus, the best state-of-the-art result was obtained (F1-measure = 92%). • The development of a new methodology for hate speech detection. • Portuguese hate speech detection using Cross-Lingual Learning. • Up to 20% performance improvement over other models using the OffComBr-2 corpus. [ABSTRACT FROM AUTHOR]

Many intelligent systems in business, government or academy process natural language as an input during inference or they might even communicate with users in natural language. The natural language processing is currently often done with machine learning models. However, machine learning needs training data and such data are often scarce for low-resource languages. The lack of data and resulting poor performance of natural language processing can be solved with cross-lingual learning. Cross-lingual learning is a paradigm for transferring knowledge from one natural language to another. The transfer of knowledge can help us overcome the lack of data in the target languages and create intelligent systems and machine learning models for languages, where it was not possible previously. Despite its increasing popularity and potential, no comprehensive survey on cross-lingual learning was conducted so far. We survey 173 text processing cross-lingual learning papers and examine tasks, datasets and languages that were used. The most important contribution of our work is that we identify and analyze four types of cross-lingual transfer based on "what" is being transferred. Such insight might help other NLP researchers and practitioners to understand how to use cross-lingual learning for wide range of problems. In addition, we identify what we consider to be the most important research directions that might help the community to focus their future work in cross-lingual learning. We present a comprehensive table of all the surveyed papers with various data related to the cross-lingual learning techniques they use. The table can be used to find relevant papers and compare the approaches to cross-lingual learning. To the best of our knowledge, no survey of cross-lingual text processing techniques was done in this scope before. [ABSTRACT FROM AUTHOR]