Towards Knowledge-Based Personalized Product Description Generation in E-commerce
Table 5. Model ablations on 3 model components.
['Model', 'BLEU', 'Diversity (n=4) (×106)']
Ablation Study This ablation study is helpful for understanding the impacts of different components on the overall performance. The components are 1) knowledge, referring to Knowledge Incorporation; 2) user category, referring to the fusion of user category; 3) aspect, referring to the fusion of aspect. We remove one component successively from KOBE until all are removed. Removing knowledge increases the BLEU score from 7.6 to 8.2 but decreases the diversity from 15.1 to 11.1. The external knowledge that is not highly relevant to the product can harm the model’s performance in BLEU. Yet it can significantly bring more contents and thus increase the diversity. Removing user category decreases BLEU sharply from 8.2 to 7.6, and still significantly decreases the diversity from 11.1 to 9.6. Removing aspect still causes a decrease in BLEU, from 7.6 to 7.2, and a decrease in diversity, from 9.6 to 7.8. These results demonstrate that the attributes do not negatively affect the model’s performance in BLEU but make an important contribution to the diversity. In brief, the three components jointly bring a significant improvement to the informativeness of KOBE’s generation without sacrificing the performance in BLEU.