Models for affective text generation have shown a remarkable progress, but they commonly rely only on basic emotion theories or valance/arousal values as conditions. This is appropriate when the goal is to create explicit emotion statements ("The kid is happy."). Emotions are, however, commonly communicated implicitly. For instance, the emotional interpretation of an event ("Their dog died.") does often not require an explicit emotion statement. In psychology, appraisal theories explain the link between a cognitive evaluation of an event and the potentially developed emotion. They put the assessment of the situation on the spot, for instance regarding the own control or the responsibility for what happens. We hypothesize and subsequently show that including appraisal variables as conditions in a generation framework comes with two advantages. (1) The generation model is informed in greater detail about what makes a specific emotion and what properties it has. This leads to text generation that better fulfills the condition. (2) The variables of appraisal allow a user to perform a more fine-grained control of the generated text, by stating properties of a situation instead of only providing the emotion category. Our Bart and T5-based experiments with 7 emotions (Anger, Disgust, Fear, Guilt, Joy, Sadness, Shame), and 7 appraisals (Attention, Responsibility, Control, Circumstance, Pleasantness, Effort, Certainty) show that (1) adding appraisals during training improves the accurateness of the generated texts by 10 pp in F1. Further, (2) the texts with appraisal variables are longer and contain more details. This exemplifies the greater control for users.

翻译：情感文本生成模型取得了显著进展，但通常仅依赖基础情绪理论或效价/唤醒度作为条件。当目标是生成显式情感表述（"这孩子很高兴。"）时，这种方法是适当的。然而，情感通常以隐式方式传达。例如，对事件的情感解读（"他们的狗死了。"）通常不需要显式的情感陈述。在心理学中，评估理论解释了事件认知评估与潜在情感发展之间的联系。该理论聚焦于对情境的评估，例如个体对事件的控制度或责任归属。我们提出假设并通过实验证明：将评估变量作为生成框架的条件具有两个优势。(1) 生成模型能更细致地理解特定情感的构成要素及其属性，从而生成更符合条件约束的文本。(2) 评估变量允许用户对生成文本进行更细粒度的控制——通过描述情境属性而非仅提供情感类别。我们基于Bart和T5的实验涵盖7种情感（愤怒、厌恶、恐惧、内疚、喜悦、悲伤、羞耻）与7种评估维度（注意力、责任、控制、情境、愉悦度、努力程度、确定性）。结果表明：(1) 在训练中引入评估变量使生成文本的准确率在F1分数上提升10个百分点；(2) 包含评估变量的文本更长且包含更多细节，体现了用户对文本更强的控制能力。