## 4 Related Work  
Many studies have addressed the needs of non-standard varieties of languages. To support the research, publicly available resources were developed simultaneously [5, 6, 7, 8, 9]. Text normalization has been tackled with a variety of methods. The earliest approaches were rule-based, with rules being created manually for one particular language [10]. Distance-based methods were adopted from the information retrieval context since measures that compare two word forms are a natural fit for matching a search term with relevant word forms in a document with non-standard language. Levenshtein distance was a common choice. Weights of edit steps were learned from a training set of normalizations [11]. In [12], a context-aware generic normalization method to handle non-standard words in social media text was proposed. For a long time, character-based statistical machine translation (SMT) was the state-of-the-art approach for normalization. For example, character-based SMT was used for the normalization of historical texts and user-generated content in the Slovenian language [13]. Over time, statistical approaches for sequence-to-sequence problems changed to neural encoder-decoder models, which resulted in remarkable improvements for many tasks, especially neural machine translation (NMT) [14]. However, neural architectures for normalization tasks did not consistently achieve this level of success. In [15], long short-term memory (LSTM) was better than the statistical approach in historical French normalization. On the contrary, neural network-based models brought worse results than statistical models in [16]. In [17], the decoding stage of a plain encoder-decoder model was modified to include target-side language models operating at different levels of granularity: characters and words. The approach resulted in an improvement over the character-level statistical machine translation. LSTM architecture for normalization German was used in [18]. Transformer architecture was reported to outperform LSTM for many NLP tasks. Contrary, in [19], LSTM brought better results than a transformer for normalizing Finnish dialects. The authors in [20] compared word-based and character-based SMT models with more advanced neural architectures and found that, in general, SMT outperformed the others when modernizing historical Spanish. This was especially true for the smallestcorpora, in which systems based on neural architectures were not able to learn properly and yielded more spelling differences than the ones contained in the original document. In [21], a user study was performed to evaluate SMT and NMT approaches to modernize Spanish historical documents and, thus, increase their accessibility to a broader audience. The NMT approach made more errors, and the user chose its modernized versions as the best option fewer times than with the SMT approach. The Arabic language is a collection of multiple variants, among which modern standard Arabic is considered the de facto standard written form. In [22], conventional orthography for Arabic dialect was defined accompanied by the corpus of five Arabic dialects to enable research on spelling correction and text normalization. Research indicates that conclusions about which approach is most suitable for language normalization often depend on the natural language itself that is being normalized, or at least the language family to which the language belongs. In [2], a shared task on multilingual lexical normalization was defined, which contains 12 language variants of Indo-European languages. The participants' results showed that normalization positively affects dependency parsing and part-of-speech tagging. The problem of low-resourced languages is data scarcity. These languages usually have only small parallel corpora of non-standard sentences aligned with standard counterparts available. In [23], researchers address the problem of low-resourced Estonian language.