To demonstrate that our purposed model indeed achieves the state-of-the-art review generation performance, we implement various evaluation metrics, including distribution-based Log-Likelihood and Perplexity, coherence-based Word Mover Distance (WMD) (Kusner et al., 2015), ngram-based BLEU (Papineni et al., 2002) and ROUGE (Lin, 2004), contextual label accuracy and human evaluation to measure the performance of review generation.
following the same metric as (Dong et al., 2017), we use sentiment accuracy, the ratio of the reviews whose sentiment matches with the given label, as an important indication of the personalization ability of the generator.