## 8 Discussion
In contrast, when compared to LaBSE_fine_tuning, the differences are not statistically significant with the multilingual dataset (\(p=0.09\)) and remain insignificant with the crosslingual dataset (\(p=0.03\)). However, ranking third does not necessarily imply that XLM-R_fine_tuning is not effective; it is simply less suitable for our specific problem. The fourth-ranked approach, LaBSE_LangChain_k1, shares similarities with other methods using LaBSE as a sentence vectorizer, with negligible differences between multilingual and crosslingual results. We performed a one-sample \(t\)-test to compare the performance of LaBSE_LangChain_k1 against LaBSE+FFNN, yielding \(p\)-values of 0.007 and 0.01 for MM18x58 and MM18x58_En, respectively. Thus, LaBSE_LangChain_k1 significantly underperforms compared to the top-ranked method in both multilingual and crosslingual scenarios. Moreover, all other memory-based approaches yielded even worse results than LaBSE_LangChain_k1, underscoring that relying solely on storing training instances' vectors may not be the most efficient strategy. Given the broad range of topics, it is presumed that the distribution of training instances in the semantic space is also broad. Hence, classifiers capable of extracting relevant information from text data through their layers are more  
Figure 4: Macro-average F1 score values (with confidence intervals) achieved on the MM18x58 dataset with the LaBSE+FFNN approach. advisable. Further analysis of memory-based methods reveals that selecting the closest instance tends to be more effective than majority voting among the 10 closest instances. Experiments confirm LaBSE's superiority over ADA. Notably, ADA_LangChain_k10_mv was the only method that performed better in crosslingual experiments compared to multilingual ones. However, this does not necessarily prove that ADA is more suitable for crosslingual experiments, as its overall result is worse than that of other methods. The performance gap between multilingual and crosslingual results using Davinci_fine_tuning is substantial, with multilingual performance being notably better. The significant discrepancy between Davinci_fine_tuning and the top-performing method, LaBSE+FFNN, suggests that Davinci_fine_tuning is not well-suited for addressing our problem. Additionally, this approach is not suitable for integration into our media monitoring system, as it violates the requirement for autonomous operation since the trained model must be stored on OpenAI's servers and incurs a fee for its usage. The least effective method for our problem is BERT+CNN. Unlike LaBSE, which is designed specifically to capture crosslingual nuances, BERT's performance in multilingual contexts is notably and statistically significantly worse. Our application of BERT as a word vectorizer, which produces sequence vectors by concatenating word embeddings, fails to adequately adapt to the varied word orders in sentences across languages, diminishing its ability to learn patterns accurately. Consequently, we strongly recommend against using this approach for problems of the nature encountered in our case. While a direct comparison of our results with other research works is challenging due to differing experimental conditions, some of our findings are consistent with those identified by previous researchers. These findings highlight the superiority of sophisticated transformer-based approaches. For instance, multilingual (and crosslingual) models such as XLM-R or LaBSE have demonstrated superior performance compared to multilingual BERT, as discussed in [18]; the superiority of XLM-R is also affirmed in [19]. Although LaBSE is not usually the best option in other studies, it excels in our context primarily because of the limited number of languages tested. LaBSE, serving as the crosslingual model fine-tuned on bilingual English-other language sentences (as shown in Figure 7 in [34]), supports 109 languages--more than are included in our dataset.