Generating response with both coherence and diversity is a challenging task in generation-based chatbots. It is more difficult to improve the coherence and diversity of dialog generation at the same time in the response generation model. In this article, we propose an improved method that improves the coherence and diversity of dialog generation by changing the model to use gamma sampling and adding attention mechanism to the knowledge-guided conditional variational autoencoder. The experimental results demonstrate that our proposed method can significantly improve the coherence and diversity of knowledge-guided conditional variational autoencoder for response generation in generation-based chatbots at the same time.

中文翻译：

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学习在基于代的聊天机器人中平衡响应生成的一致性和多样性

在基于代的聊天机器人中，生成具有一致性和多样性的响应是一项具有挑战性的任务。在响应生成模型中同时提高对话生成的一致性和多样性比较困难。在本文中，我们提出了一种改进的方法，通过将模型更改为使用伽马采样并将注意力机制添加到知识引导的条件变分自动编码器，从而提高对话生成的连贯性和多样性。实验结果表明，我们提出的方法可以同时显着提高知识引导的条件变分自动编码器在基于生成的聊天机器人中用于响应生成的一致性和多样性。