Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction
Table 5: Experiment results of adding the transferred encoder or auxiliary learning on PE-BiLSTM(%).
['Models', '14res P', '14res R', '14res F1', '14lap P', '14lap R', '14lap F1', '15res P', '15res R', '15res F1', '16res P', '16res R', '16res F1']
Compared to the base model PE-BiLSTM, we can find that PE-BiLSTM+transferred encoder and PE-BiLSTM+auxiliary learning both achieve significant and consistent improvements on all datasets. The comparisons validate the effectiveness of transferring the encoder and latent opinion words from sentiment classification model for the TOWE task. After integrating both the transferred encoder and auxiliary learning, the model LOTN obtains further improvements. The results indicate that the proposed two methods are useful for the final model LOTN and they transfer opinions knowledge from different perspectives.