DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction
Table 2: F1 score (%) comparison of all systems for aspect term-polarity pair extraction.
['[EMPTY]', '[BOLD] Model', 'SL', 'SR', 'ST']
Comparison Results. As the results show, our DOER obtains consistent improvement over baselines. Compared to the best pipelined model, the proposed framework outperforms DE-CNN+TNet by 3.88%, 5.24%, and 2.63% on SL, SR, and ST, respectively. It indicates that an elaborated joint model can achieve better performance than pipeline approaches on aspect term-polarity co-extraction task. Besides, seven collapsed models are also introduced to the comparison. Compared to the best of these collapsed approaches, DOER improves by 2.36%, 2.87%, and 2.24% over E2E-TBSA on SL, SR, and ST, respectively. This result shows the potential of a joint model which considers the interaction between the two relevant tasks. Comparing with existing works based on the joint approach, i.e., CRF-joint and NN+CRF-joint, DOER makes substantial gains over them as well. The improvements over DE-CNN+TNet and E2E-TBSA are statistically significant (p<0.05). Ablation Study. The fact that S-BiReGU gives superior performance compared to S-BiLSTM indicates the effectiveness of ReGU in our task. This residual architecture enables information transfer to the next layers more effective. With the help of CSU, S-BiReGU+CSU achieves better performance than without it. We believe the interaction of information between ATE and ASC is essential to improve each other. Although the samples with long aspect terms are rare, the auxiliary task of aspect term length can improve the performance. Another auxiliary task of sentiment lexicon can also enhance the representation of the proposed framework. As a whole of S-BiReGU, CSU, AuL, and AuS, the proposed DOER achieves superior performance. It mainly benefits from the enhanced features by the two auxiliary tasks and the interaction of two separate routes of ATE and ASC.