## 2 Related Work
For instance, knowledge acquired through the Expectation-Maximization (EM) algorithm and refined via semi-supervised learning techniques has shown potential in enhancing performance across diverse linguistic pairs, such as English to Chinese and English to French, providing sub stantial improvements over methods dependent solely on direct machine translation [21]. Furthermore, a novel teacher-student approach provides 'weak' supervision in the target language using a curated set of translated seed words, which facilitates learning by contextualizing these seed words within unlabeled texts. This method has demonstrated its efficacy across an impressive range of 18 languages, outperforming established models like mBERT [22]. Neural networks also play a pivotal role in advancing crosslinking solutions. One approach involves using a CNN-based classifier trained in a source language with labeled data, which is then adapted using unlabeled data from a parallel corpus in the target language. Soft labels derived from this corpus help train a classifier for the target language, proving effective in experimental setups involving multiple language pairs [23]. Additionally, pretrained multilingual encoders have been utilized to create universal sentence representations that can be shared across languages, facilitating the prediction of target classes in various linguistic settings [24]. Another sophisticated strategy combines language-invariant and language-specific features using adversarial networks and a mixture-of-experts model. This dynamic approach establishes a nuanced similarity between target and source languages, enhancing the overall classification performance across multiple languages more effectively than conventional models [25]. Moreover, innovative data augmentation techniques that incorporate multilingual code-switching have been developed to support the language-agnostic capabilities of models. These techniques have been applied successfully in a setup that includes nine languages, with LSTM and mBERT classifiers, where mBERT demonstrated superior crosslingual transfer capabilities [26]. In a similar approach, methods using graph convolutional networks to manage heterogeneous information within and across languages have shown significant promise. These models surpass traditional approaches like mBERT or XLM-R in various tasks spread over six languages [27]. The flexibility and robustness of XLM-R have also been validated in rule-based, dictionary-based, zero-shot, few-shot, and supervised learning environments across multiple datasets, solidifying its reputation as a formidable crosslingual tool [28]. Further expanding the capabilities of multilingual models, recent studies have enhanced mBERT and XLM-R with language-independent Wiki entities to tackle the challenges of crosslinguality more effectively. These enriched models have demonstrated impressive performance in topic classification tasks involving up to twelve languages [29]. Moreover, a cutting-edge teacher-student framework has been explored to improve both supervised and zero-shot performances of multilingual models. This framework strategically employs multiple source languages and target languages, showcasing a sophisticated method to optimize multilingual model training [30]. In conclusion, our exploration into crosslingual modeling highlights a range of innovative approaches that effectively address the inherent complexities of language processing across diverse linguistic environments. These advancements improve the practical applications of language models and pave the way for future innovations in handling multilingual and crosslingual data more efficiently. In addition to closed-set classification, we also explore the open-set text classification problem. Previously presented zero-shot techniques expand the range of potential topics but require prior knowledge of the topics to search for.