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.

中文翻译：

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情绪可控聊天机器人的研究

传统的 seq2seq 聊天机器人模型仅尝试以输入序列为条件查找概率最高的句子，而不考虑输出句子的情绪。在本文中，我们研究了四种模型来扩展或调整聊天机器人响应的情绪：基于角色的模型、强化学习、即插即用模型和 CycleGAN，所有模型都基于 seq2seq 模型。我们还开发了机器评估指标来估计给定输入的响应是否合理。这些指标与人工评估一起用于从不同方面分析四种模型的性能；强化学习和 CycleGAN 被证明非常有吸引力。