# Transferring Sentiment Cross-Lingually within and across Same-Family Languages
## 9 Conclusions  
We have presented our framework for multitask cross-lingual sentiment classifier transfer. We evaluated seven official Slavic languages using a model trained with multiple language resources. We discovered that the transfer of sentiment knowledge is enhanced within the same language family, i.e., the closer the language, the easier the transfer, given a large dataset. We also discovered that a large training dataset from a distant language family can outperform smaller datasets from similar languages. Consequently, datasets from the same language family and distant language families can be utilized to combat the issue of inadequate resources. Furthermore, we transform the fine-grained sentiment problem into a 3-class problem by multi-task training, leading to improved scores. A potential study limitation may arise from the limited amount of the dataset utilized to evaluate the effectiveness of the trained model. Although substitutes cannot replace the test set in the target language, low-resourced languages inherently have fewer supervised examples. This also leads us to the conclusion that ensuring the presence of supervised resources in each language is crucial, and efforts should be focused on providing datasets and corpora for languages that lack sufficient resources. Furthermore, these findings may be specific to classification tasks and may not be generalizable to other tasks, although this can only be confirmed by empirical verification. This study suggests that languages with limited data can depend on datasets from other languages that have more resources. Our work employs the XLM-R language model, which can be readily trained on a consumer-grade GPU (or Google Colab). A recent study [124] has demonstrated that large language models such as Llama 2 and ChatGPT, while exhibiting comparable performance, possess substantial hardware demands and deployment constraints that are absent in our situation. Future work can be divided into four distinct paths. Firstly, the performance enhancement might be computed by adding more datasets from high-resourced languages. Additionally, an examination of several language families could be conducted to determine if the inclusion of a specific distant family language enhances the overall performance. Furthermore, another element related to the dataset is the quality of the text and annotations. A study conducted independently could calculate the information value contained within a specific supervised instance and its impact on transfer learning. Lastly, our hypothesis is that the process of classification is indirectly influenced by the sharing of sub-word tokens and, as a result, certain dataset combinations facilitate sentiment transmission in languages that are part of the same language family. We intend to empirically verify this assertion through additional experiments. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Languages** & **Hr** & **Cs** & **PI** & **Ru** & **Sk** & **Bg Latin** & **Ru Latin** & **Sv** & **En** \\ \hline Bulgarian & 130 & **235** & 90 & **2919** & 123 & 261 & 126 & 122 & **215** \\ Croatian & & **5075** & **2881** & 432 & 2215 & 1778 & 3014 & 4420 & 3256 \\ Czech & & & 9656 & 1300 & 6035 & 3573 & 8733 & **10,075** & **15,122** \\ Polish & & & & 690 & 2927 & **2207** & **5075** & 5417 & 6931 \\ Russian & & & & & 371 & 314 & 1522 & 733 & 1207 \\ Slovak & & & & & & 1616 & 2923 & 3412 & **2774** \\ Bulgarian (Latin) & & & & & & & 2689 & 2655 & **2416** \\ Russian (Latin) & & & & & & & & 5799 & 5702 \\ Slovene & & & & & & & & 6352 \\ English & & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 8: Languages and number of shared tokens in their training set. Croatian-Hr, Czech-Cs, Polish-PI, Russian-Ru, Slovak-Sk, Slovene-Sv, Bulgarian-Bg, English-En. The language combinations that perform the best are indicated by bold numerals. **Author Contributions:** Conceptualization, G.T. and N.M.P. ; methodology, G.T. and N.M.P. ; software, G.T. ; validation, G.T. and N.M.P. ; formal analysis, G.T. and N.M.P.