Towards Controllable and Personalized Review Generation
Table 1: Comparison of Experimental Results on Amazon Review Dataset (** stands for significance under 99% confidence, * stands for 95% confidence)
['Models', 'Log-Likelihood', 'WMD', 'PPL', 'BLEU-4(%)', 'ROUGE-L(%)']
To illustrate the superiority and generalizability of our RevGAN model, we implement our model on three different domains of the Amazon Review Dataset including musical instruments, automotive and patio products. On average, we could witness a 5% increase in Word Mover Distance (WMD), 80% improvement in BLEU and 10% rising in ROUGE. Besides, the comparison between different variations of RevGAN model verifies that indeed the combination of all three novel components gives the best generation performance. By deploying bootstrap re-sampling techniques introduced in the previous section, we conduct hypothesis tests where all the tests confirm the significant improvement of our RevGAN model. In that sense, we claim that our model achieved the state-of-the-art results on review generation. We also showcase some generated reviews at the end of this section.