Investigation of Sentiment Controllable Chatbot
TABLE IX: Sentence reconstruction of plug and play models with different vocabulary truncation proportions on Chinese CECG dataset.
['Original input', 'Original input', '禮貌能提高一個的素質 Courtesy can improve quality']
The 0.0 output also seems reasonable, although it fails to reconstruct the original sentence: it replaces “禮貌” (“courtesy”) with “動作” (“action”), probably because it depends more on the decoder language model. With 0.7 truncation, the model outputs an incorrect sentence, replacing “提高” (“improve”) with “以” (“by”); this could be because the many unknown words hinder the model from learning correct sentence grammar. The model with KL cost annealing and with a vocabulary truncation of 0.3 outperforms all others, so we will use this setting in the following experiments.