论文标题
对情感可控聊天机器人的调查
Investigation of Sentiment Controllable Chatbot
论文作者
论文摘要
常规的SEQ2SEQ聊天机器人模型只试图找到以最高概率在输入序列中的句子，而无需考虑输出句子的情感。在本文中，我们研究了四个模型，以扩展或调整聊天机器人响应的情感：基于角色的模型，加固学习，插头和游戏模型以及Cyclegan，都是基于SEQ2SEQ模型。我们还开发了机器评估的指标，以估计鉴于输入的响应是否合理。这些指标以及人类评估，用于分析四个模型的性能。增强学习和自行车gan被证明非常有吸引力。
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.
