Relational Graph Attention Network for Aspect-based Sentiment Analysis
Table 4: Results of ablation study, where “Ordinary” means using ordinary dependency trees, “Reshaped” denotes using the aspect-oriented trees, and “*-n:con” denote the aspect-oriented tree without using n:con.
['Tree', 'Method', 'Restaurant', 'Laptop', 'Twitter']
Ablation Study We further conduct an ablation study to evaluate the influence of the aspect-oriented dependency tree structure and the relational heads. We present the results on ordinary dependency trees for comparison. Furthermore, after removing the virtual relation n:con, the performance of R-GAT drops considerably. We manually examined the misclassified samples and found that most of them can be attributed to poor parsing results where aspects and their opinion words are incorrectly connected. This study validates that adding the n:con relation can effectively alleviate the parsing problem and allows our model to be robust. In this paper, the maximal number of n is set to 4 according to empirical tests. Other values of n are also explored but the results are not any better. This may suggest that words with too long dependency distances from the target aspect are unlikely to be useful for this task.