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 4: Accuracy comparison with TST.
['[EMPTY]', 'Neg. to Pos.', 'Pos. to Neg.']
We also compare our model with TST for sentiment polarity transfer. We employ the same evaluation metric as used in Shen et al. the sentiment accuracy of the transferred sentences. We define the revised sentences with ratings larger than 3 as positive, smaller than 3 as negative. The results show that our model achieves better accuracy than TST in both transfer directions. One reason is that our method models the associations between each sentence and its outcome, and thus captures the sentiment wordings better. Our model is far better for transferring negative sentences into positive, moreover, both models achieve better performance for this transfer direction. We can probably attribute the reason to the imbalanced training data: 55% positive sentences v.s. 45% negative sentences.