Investigation of Sentiment Controllable Chatbot
TABLE VI: Evaluation of reinforcement learning models with different reward combinations at 2500 iterations on Chinese CECG dataset.
['Input Seq2seq(baseline)', 'Input Seq2seq(baseline)', 'Input Seq2seq(baseline)', 'COH1 -8.6', 'COH2 0.664', 'SCL 0.33', 'LM -1.574']
In the first three sets of experiments, we fixed R2 as 0.0 and adjusted the proportion of R1 and R3 to determine how the four metrics were affected. Interestingly, COH2 degrades compared to the pretrained MLE baseline, showing that the RL model is highly goal-oriented – to improve COH1 and SCL, it sacrifices COH2 performance. The result also implies that COH1 and COH2 are nearly mutually independent as the increase of R1 seems to little affect COH2. The LM score, on the other hand, generally is improved comparing with the typical seq2seq model: this is perhaps attributable to R1, which also takes into account word ordering. In the last set of experiments, we increase R2 to 0.3 to remedy the COH2 score. The result shows that COH1 and COH2 are close to the baseline; at the same time, though, the SCL and LM scores improve.