Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models
Table 5: Evaluation of diversity of 1000 generated sentences on self-BLEU scores (B-n), unique n-gram percentages (ngr), 2-gram entropy score.
['[BOLD] Model', '[BOLD] Yelp  [BOLD] B-2', '[BOLD] Yelp  [BOLD] B-3', '[BOLD] Yelp  [BOLD] B-4', '[BOLD] Yelp  [BOLD] 2gr', '[BOLD] Yelp  [BOLD] 3gr', '[BOLD] Yelp  [BOLD] 4gr', '[BOLD] Yelp  [BOLD] Etp-2']
A small self-BLEU score together with a large BLEU score can justify the effectiveness of a model, i.e., being able to generate realistic-looking as well as diverse samples. Among all the VAE variants, ml-VAE-D shows the smallest BLEU score and largest unique n-grams percentage, demonstrating the effectiveness of hieararhically structured generative networks as well as latent variables. Even though AAE and ARAE yield better diversity according to both metrics, their corpus-level BLEU scores are much worse relative to ml-VAE-D. We leverage human evaluation for further comparison.