  Conventional seq2seq chatbot models attempt only to find sentences with the
highest probabilities conditioned on the input sequences, without considering
the sentiment of the output sentences. In this paper, we investigate four
models to scale or adjust the sentiment of the chatbot response: a
persona-based model, reinforcement learning, a plug and play model, and
CycleGAN, all based on the seq2seq model. We also develop machine-evaluated
metrics to estimate whether the responses are reasonable given the input. These
metrics, together with human evaluation, are used to analyze the performance of
the four models in terms of different aspects; reinforcement learning and
CycleGAN are shown to be very attractive.
