An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis
Table 3: Model comparison. 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.
['[EMPTY]', 'Methods', 'CMLA-ALSTM', 'CMLA-dTrans', 'DECNN-ALSTM', 'DECNN-dTrans', 'PIPELINE', 'MNN', 'INABSA', 'IMN− [ITALIC] d wo DE', 'IMN− [ITALIC] d', 'IMN wo DE', 'IMN']
Main results. Note that IMN performs co-extraction of aspect and opinion terms in AE, which utilizes additional opinion term labels during training, while the baseline methods except CMLA do not consider this information in their original models. To enable fair comparison, we slightly modify those baselines to perform co-extraction as well, with opinion term labels provided. Specifically, for AE (F1-a and F1-o), IMN−d performs the best in most cases. For AS (acc-s and F1-s), IMN outperforms other methods by large margins. PIPELINE, IMN−d, and the pipeline methods with dTrans also perform reasonably well on this task, outperforming other baselines by moderate margins. All these models utilize knowledge from larger corpora by either joint training of document-level tasks or using domain-specific embeddings. This suggests that domain-specific knowledge is very helpful, and both joint training and domain-specific embeddings are effective ways to transfer such knowledge. 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.