An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese
Table 2: Rakuten test results, in error percentages. Best results from other models (GlyphNet to Super Character) obtained from Zhang and LeCun (2017) and Sun et al. (2018)
['Model', 'Rakuten Binary', 'Rakuten Full']
We trained ELMo+BCN and ULMFiT on the Rakuten datasets for 10 epochs each and selected the one that performed best. Since BERT fine-tunes all of its layers, we only train for 3 epochs as suggested by Devlin et al. All transfer learning-based methods outperform previous methods on both datasets, showing that these methods still work well without being fine-tuned on target corpora.