# Zero-Shot Learning for Cross-Lingual News Sentiment Classification
## 6 Results  
This section presents the results of the experiments conducted in the course of this study. We first present the results of the document representation approaches. The results are presented in Table 2. Next, for the best performing representation approach, we test our newly introduced technique for sentiment classification with intermediate training, and the results with and without the intermediate training objective are compared in Table 3. We also compare our results with the previous sate-of-the-art SVM and Naive Bayes models on the Slovenian dataset from [3], as well as with the neural network model based on LSTMs and TF-IDF from [39]. We note, however, that the testing regime in these experiments was not the same. In [3], the authors tested their models using five times 10-fold cross-validation, while in [39], the model was trained and tested on a random train-test split of the whole dataset with an 80:20 train-test split ratio. For this reason, the results are not directly comparable. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & \multicolumn{2}{c}{**Slovenian Cross-Validation**} & \multicolumn{3}{c}{**Croatian Test Set**} & **Average** \\ \hline  & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** & **F1** \\ \hline Majority classifier & 17.34 & 33.33 & 22.76 & 0.20 & 0.33 & 25.00 & / \\ \hline Beginning of the document & 65.45 \(\pm\) 2.61 & **62.83**\(\pm\) 2.46 & 63.34 \(\pm\) 2.29 & 57.74 \(\pm\) 1.20 & **53.91**\(\pm\) 2.41 & 52.06 \(\pm\) 2.64 & 57.70 \\ \hline Beginning and end of the document & 64.72 \(\pm\) 2.82 & 62.67 \(\pm\) 2.69 & 63.33 \(\pm\) 2.56 & **59.00**\(\pm\) 1.62 & 53.53 \(\pm\) 3.64 & **52.41**\(\pm\) 2.58 & **57.87** \\ \hline \multicolumn{8}{c}{Sequences from every part of the document} \\ \hline Most informative subsequence & 64.42 \(\pm\) 2.44 & 62.09 \(\pm\) 2.27 & 63.00 \(\pm\) 2.34 & 57.87 \(\pm\) 1.32 & 53.23 \(\pm\) 2.82 & 52.30 \(\pm\) 2.86 & 57.65 \\ \hline Averaging subsequence representations & **66.50**\(\pm\) 3.13 & 62.00 \(\pm\) 2.45 & **63.39**\(\pm\) 2.42 & 57.53 \(\pm\) 1.14 & 52.95 \(\pm\) 3.38 & 51.55 \(\pm\) 3.93 & 57.47 \\ \hline \(1\)D CNN & 63.96 \(\pm\) 10.02 & 60.91 \(\pm\) 5.22 & 61.58 \(\pm\) 7.78 & 54.96 \(\pm\) 5.48 & 53.31 \(\pm\) 3.62 & 50.28 \(\pm\) 4.65 & 55.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the document representation approaches. The first column shows the performance of models in the Slovenian 10-fold cross-validation setting; the second column is the average zero-shot performance on the Croatian test set; and the last column presents the average F1 score of the results on the Slovenian and Croatian datasets. Best results are marked in bold. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & \multicolumn{2}{c}{**Slovenian**} & \multicolumn{2}{c}{**Crosian**} & \multicolumn{2}{c}{**Average**} \\ \hline  & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** & **F1** \\ \hline Majority classifier & 17.34 & 33.33 & 22.76 & 0.20 & 0.33 & 25.00 & / \\ \hline \multicolumn{6}{c}{Reported results from related studies} \\ \hline SVM (from [PERSON] et al. [3]) 5 \(\times\) 10 CV & / & / & 63.42 \(\pm\) 1.96 & / & / & / & / \\ \hline NBM(from [PERSON] et al. [3]) 5 \(\times\) 10 CV & / & / & 65.97 \(\pm\) 1.70 & / & / & / & / \\ \hline LSTM+TF-IDF (from Pelicon [39]train-set split & / & / & 62.5 & / & / & / & / \\ \hline \multicolumn{6}{c}{Results from the current study} \\ \hline Beginning of the document & 65.45 \(\pm\) 2.61 & 62.83 \(\pm\) 2.46 & 63.34 \(\pm\) 2.29 & **57.74**\(\pm\) 1.20 & 53.91 \(\pm\) 2.41 & 52.06 \(\pm\) 2.64 & 57.70 \\ \hline Beginning and end of the document with sentiment intermediate training & **67.19**\(\pm\) 2.67 & **66.00**\(\pm\) 3.00 & **66.33**\(\pm\) 2.60 & 56.32 \(\pm\) 1.88 & **54.90**\(\pm\) 2.36 & **54.77**\(\pm\) 1.39 & **60.55** \\ \hline \hline \end{tabular}
\end{table}