## 8 Discussion  
The first part of the Section 8, focusing on the multi-class closed-set classification experiments and their outcomes, will enable us to address the first research question (outlined in the concluding part of Section 2). We will achieve this by breaking it down into more detailed sub-questions:  
1. How effective is the best solution to this text classification problem? 2. Which scenario--multilingual or crosslingual--is more suitable for solving our problem? This will determine whether machine translation into English is necessary or whether methods can be applied directly to the original texts in various languages. 3. Which predicted categories are the least accurately predicted, and why are these shortcomings? Figure 2: Integrated pipeline for topic classification (_known_ classes) and clustering (_new_ classes). 4. Which type (i.e., trainable/fine-tunable, memory-based, or generative) and which of the tested approaches are most recommended for solving our problem, and which are not? The comparative multi-class closed-set topic classification experiments determined the superiority of the LaBSE+FFNN (i.e., optimized FFNN applied on top of LaBSE vectorization) over the other tested memory-based approaches (i.e., LaBSE_LangChain_k1, LaBSE_LangChain_k10_mv, ADA_LangChain_k1, and ADA_LangChain_k10_mv), trainable/fine-tunable approaches (BERT+CNN, XLM-R_fine_tuning, and LaBSE_fine_tuning), and LLM-based generative approaches (i.e., Davinci_fine_tuning). Under multilingual and crosslingual scenarios, the LaBSE+FFNN approach achieves the best macro-averaged F1 score values, which are \(\sim\)0.944 \(\pm\) 0.015 and \(\sim\)0.946 \(\pm\) 0.019, respectively. The FFNN part of this approach was optimized using Hyperas and Hyperopt, and the best architecture is presented in Figure 3. Our multilingual and crosslingual experiments produced nearly identical results (the differences are insignificant with the \(p\)-value \(\gg\) 0.05) with LaBSE+FFNN, affirming the robustness of this multilingual and crosslingual LaBSE language model. This leads us to conclude that machine translation of texts into English is unnecessary, as it does not significantly impact the results. Zooming into the results for different topics (see Figure 4) reveals that the worst performance is observed in the ClimateAction, EnergyMarketsandStrategies, and ForgeryMoney topics. This outcome is unsurprising given our training dataset's limited coverage of these topics. Manual error analysis revealed that ClimateAction is frequently confused with a  
Figure 3: The optimal architecture of the LaBSE+FFNN approach that was determined using Hyperas and Hyperopt. similar ClimateChange topic. Additionally, the accuracy of the best-covered NATO topic reaches approximately 95%, indicating that augmenting training data for less-covered topics would likely enhance prediction accuracy. The second-best performing method was LaBSE_fine_tuning, which required the adjustment of approximately 490 million parameters, while LaBSE+FFNN required only around 1.3 thousand. The minimal difference in F1 score values with MM18x58 and MM18x58_En datasets further underscores LaBSE's strength in both multilingual and crosslingual scenarios. The calculated \(p\)-values between the results of LaBSE_fine_tuning and LaBSE+FFNN exceeded 0.1, indicating that the observed differences lack statistical significance. This allows us to conclude that the utilization of LaBSE as a sentence vectorizer eliminates the need to unfreeze and adjust all its parameters. It appears that LaBSE already possesses sufficient knowledge about languages, including their vocabularies, sentence structures, and domain-specific information, thereby leaving us with the sole task of adapting it to our specific topic classification problem. The XLM-R_fine_tuning method, a popular choice, ranks third in our experiments. Compared to LaBSE+FFNN, significant differences emerge with \(p\)-values of 0.02 and 0.004 in multilingual and crosslingual experiments, respectively.