Efficient text generation of user-defined topic using generative adversarial networks
Table 2: The probability of sentiment tendency of generated sentences
['[EMPTY]', '[BOLD] Positive', '[BOLD] Negative', '[BOLD] Neutral']
With training discriminator-special, UD-GAN (G+S) and UD-GAN (S) are more likely to generate positive sentences than baselines. Which proves that the proposed method is capable to generate the sentences with the desired sentiment. However, since the total number of sentences expressing positive sentimental tendency in the training corpus is quite low, the probability of UD-GAN generating positive sentiment is still not higher than 0.5.