An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese
Table 5: Low-shot learning results for the Yahoo dataset, in error percentages. Transfer learning-based methods are trained on 13 of the total dataset, while the other models are trained on the whole dataset.
['Model', 'Yahoo Binary']
Low-shot learning refers to the practice of feeding a model with a small amount of training data, contrary to the normal practice of using a large amount of data. We chose the Yahoo dataset for this experiment due to its small size. that, with only 13 of the total dataset, ULMFiT and BERT perform better than task-specific models, while BCN+ELMo shows a comparable result. Clearly, this shows that the models have learned significantly during the transfer learning process.