An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis
Table 7: Model comparison in a setting without opinion term labels. Average results over 5 runs with random initialization are reported. ∗ indicates the proposed method is significantly better than the other baselines (p<0.05) based on one-tailed unpaired t-test.
['Methods', 'D1 F1-a', 'D1 acc-s', 'D1 F1-s', 'D1 F1-I', 'D2 F1-a', 'D2 acc-s', 'D2 F1-s', 'D2 F1-I', 'D3 F1-a', 'D3 acc-s', 'D3 F1-s', 'D3 F1-I']
In this setting, we modify the proposed IMN and IMN−d to recognize aspect terms only in AE. The opinion transmission operation, which sends the opinion term predictions from AE to AS, is omitted as well. Both IMN−d and IMN still significantly outperform other baselines in most cases under this setting. Consistent improvements are not observed in other baseline methods when trained with opinion term labels. These findings suggest that knowledge obtained from learning opinion term extraction is indeed beneficial, however, a carefully-designed network structure is needed to utilize such information. IMN is designed to exploit task correlations by explicitly modeling interactions between tasks, and thus it better integrates knowledge obtained from training different tasks.