  Conventional seq2seq chatbot models only try to find the sentences with the
highest probabilities conditioned on the input sequences, without considering
the sentiment of the output sentences. Some research works trying to modify the
sentiment of the output sequences were reported. In this paper, we propose five
models to scale or adjust the sentiment of the chatbot response: persona-based
model, reinforcement learning, plug and play model, sentiment transformation
network and cycleGAN, all based on the conventional seq2seq model. We also
develop two evaluation metrics to estimate if the responses are reasonable
given the input. These metrics together with other two popularly used metrics
were used to analyze the performance of the five proposed models on different
aspects, and reinforcement learning and cycleGAN were shown to be very
attractive. The evaluation metrics were also found to be well correlated with
human evaluation.
