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Table 3: Accuracy and RMSE of competing models on polarity (P_Acc) and fine-grained (F_Acc and F_RMSE) datasets. Items in red are performances worse than the no-transfer CNN baseline. An asterisk (*) indicates that LeTraNets is significantly better than the second best model (p<0.05) .
['[EMPTY]', 'Mov_en', 'Res_en', 'Res_en', 'Res_en', 'Mov_ko', 'Mov_ko', 'Mov_ko']
Long to Short Transfer Results show that Cross Domain Transfer models do not perform well, which confirms our hypothesis that they are not well suited for this task. MILNet performs well on polarity tasks, but performs poorly on fine-grained tasks, having worse performance than the no-transfer CNN baseline. This shows that although Multiple Instance Learning models are effective in classifying positive or negative sentiments, they are not flexible to fine-grained sentiment intensities, which differs when text lengths are different. On the other hand, BaggedCNN performs better than MILNet, proving that simplifying MIL models work well on CLT. Overall, LeTraNets performs the best among all models, having the best accuracies and RMSEs on all datasets and settings.