QuaSE: Sequence Editing under Quantifiable Guidance The work described in this paper was done when Yi Liao was an intern at Tencent AI Lab. The work is partially supported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: CUHK413510)
Table 7: Ablation study.
['[EMPTY]', 'T=1', 'T=3', 'T=5']
Recall that our model is a combination of a revised VAE, which disentangles two factors from a sentence to enable the subsequent design, and a coupling component modeling pseudo-parallel sentence pairs. “None” refers to all three losses are removed, and it is basically worse than S2BS, which implies only using the revised VAE does not work well. As more losses added, the performance is gradually improved. Moreover, the dual reconstruction is more effective than the others.