Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning
Table 3: Ablation study of HRL on three different datasets.
['[BOLD] Approaches', '[BOLD] TripUser Acc.↑', '[BOLD] TripUser MSE↓', '[BOLD] TripAdvisor Acc.↑', '[BOLD] TripAdvisor MSE↓', '[BOLD] BeerAdvocate Acc.↑', '[BOLD] BeerAdvocate MSE↓']
Ablation Study. Further, we conduct the ablation study of HRL to evaluate the contribution of each component. From this table, we can see that 1) Using cosine intermediate reward in Eq.(3) can averagely improve Acc. by 2.87% on three different datasets. 2) Using penalty delay reward in Eq.(6) can averagely improve Acc. by 1.23%. 3) To concatenate additional representation of the selected clauses in rating predictor can improve Acc. by 4.38%. 4) To concatenate additional representation of the selected words in rating predictor can improve Acc. by 2.72%. 5) Using the clause splitting instead of sentence splitting could improve Acc. by 2.44%. This confirms that it is more appropriate to consider clauses as the segmentation units than sentences. This is because that 90% of clauses contain only one opinion expression as proposed in \newciteDBLP:journals/ldvf/BayoudhiGKB15. In this scenario, sentence selection will not be able to discard noisy parts inside the sentence for the aspect location or room.