The conversation task is usually formulated as a conditional generation problem, i.e., to generate a natural and meaningful response given the input utterance. Generally speaking, this formulation is apparently based on an oversimplified assumption that the response is solely dependent on the input utterance. It ignores the subjective factor of the responder, e.g., his/her emotion or knowledge state, which is a major factor that affects the response in practice. Without explicitly differentiating such subjective factor behind the response, existing generation models can only learn the general shape of conversations, leading to the blandness problem of the response. Moreover, there is no intervention mechanism within the existing generation process, since the response is fully decided by the input utterance. In this work, we propose to view the conversation task as a dual-factor generation problem, including an objective factor denoting the input utterance and a subjective factor denoting the responder state. We extend the existing neural sequence-to-sequence (Seq2Seq) model to accommodate the responder state modeling. We introduce two types of responder state, i.e., discrete and continuous state, to model emotion state and topic preference state, respectively. We show that with our dual-factor generation model, we can not only better fit the conversation data, but also actively control the generation of the response with respect to sentiment or topic specificity.

中文翻译：

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对话的双因素生成模型

对话任务通常被表述为条件生成问题，即在给定输入话语的情况下生成自然且有意义的响应。一般来说，这个公式显然是基于一个过于简单的假设，即响应完全取决于输入的话语。它忽略了响应者的主观因素，例如他/她的情绪或知识状态，这是影响实践中响应的主要因素。在没有明确区分响应背后的这种主观因素的情况下，现有的生成模型只能学习对话的一般形式，导致响应的平淡问题。此外，现有的生成过程中没有干预机制，因为响应完全由输入话语决定。在这项工作中，我们建议将对话任务视为双因素生成问题，包括表示输入话语的客观因素和表示响应者状态的主观因素。我们扩展了现有的神经序列到序列 (Seq2Seq) 模型以适应响应者状态建模。我们引入了两种响应者状态，即离散状态和连续状态，分别对情绪状态和主题偏好状态进行建模。我们表明，通过我们的双因素生成模型​​，我们不仅可以更好地拟合对话数据，而且还可以主动控制关于情绪或主题特异性的响应的生成。我们扩展了现有的神经序列到序列 (Seq2Seq) 模型以适应响应者状态建模。我们引入了两种响应者状态，即离散状态和连续状态，分别对情绪状态和主题偏好状态进行建模。我们表明，通过我们的双因素生成模型​​，我们不仅可以更好地拟合对话数据，而且还可以主动控制关于情绪或主题特异性的响应的生成。我们扩展了现有的神经序列到序列 (Seq2Seq) 模型以适应响应者状态建模。我们引入了两种响应者状态，即离散状态和连续状态，分别对情绪状态和主题偏好状态进行建模。我们表明，通过我们的双因素生成模型​​，我们不仅可以更好地拟合对话数据，而且还可以主动控制关于情绪或主题特异性的响应的生成。