# Transferring Sentiment Cross-Lingually within and across Same-Family Languages
## 6 Methodology
### Training  
A typical dataset is divided into training, testing, and validation sets in the ratio of 8:1:3. This partitioning is preferred when the dataset used to train the system is extensive. Low-resource languages have a paucity of examples (thousands of examples). By separating the few training data samples into test and validation sets, the training set is reduced further. We therefore conducted cross-validation. \(K\)-fold cross-validation randomly divides all dataset instances into \(K\) groups, where \(K\) is a predetermined number. Folds are used to denote each group. One-fold from \(K\) is selected as the test set, while the remaining \(K\)\(-\)1 subsets are used for training. This process is repeated until each fold in the dataset has been utilized as a test set. The training process will therefore be repeated k times. In our case, \(K\) was assigned the value 5. The most prevalent pattern is the transfer of knowledge to a low-resource language task using data from high-resource tasks. We investigate the use of multiple datasets from low-resource languages to enhance the performance of target languages. We conducted experiments in the following environments:  
1. Using only source-language data for fine-tuning. This is the conventional transfer learning setup performed by a source-language fine-tuning classifier. A zero-shot test is administered to the trained model using a test of the target language. We guided the training process using the target language's validation set. We projected labelsfrom a fine-grained class of 5 classes to a coarse-grained class of 3 classes due to the possibility that the target language dataset labels do not match the source language.
2. Fine-tuning with a single source and target language. We sampled training sets from multiple languages and jointly trained the classifier. We utilized datasets from distantly related languages and vice versa.
3. Fine-tuning using multiple datasets derived from a single source and target language. This is a multilingual environment with multiple sources.
4. Fine-tuning with the Latin versions of the Bulgarian and Russian datasets.  
In Tables 2 and 3, we list an assortment of experiments. The first section of Table 2 depicts the combined instruction of Slavic and English languages. The second section substitutes Russian for English. The third section only utilizes language data from a single source. The following section illustrates the compilation of various low-resource languages. The next two sections combine Czech and Polish with Slavic languages with limited resources. Bulgarian is ultimately selected as the source language for the combinations. In Table 3, the study's Latin transliterations of Bulgarian and Russian with other language combinations are displayed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{4}{c}{**Source Languages**} \\ \hline
**1 st** & **2 nd** & **3 rd** & **4 th** \\ \hline Bulgarian & English & & \\ Croatian & English & & \\ Czech & English & & \\ Polish & English & & \\ Russian & English & & \\ Slovak & English & & \\ Slovene & English & & \\ \hline Bulgarian & Russian & & \\ Croatian & Russian & & \\ Czech & Russian & & \\ Polish & Russian & & \\ Slovak & Russian & & \\ Slovene & Russian & & \\ \hline Bulgarian & & & \\ Croatian & & & \\ Czech & & & \\ Polish & & & \\ Russian & & & \\ Slovak & & & \\ Slovene & & & \\ \hline Croatian & Slovene & & \\ Croatian & Slovene & Slovak & Bulgarian \\ Croatian & Slovene & Slovak & & \\ \hline Czech & Bulgarian & & & \\ Czech & Croatian & & \\ Czech & Slovak & & \\ Czech & Slovene & & \\ \hline Polish & Bulgarian & & & \\ Polish & & & \\ Polish & & Slovene & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Language pairs in various combinations for joint training.