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.

通过加入评估变量作为条件，在生成框架中包含评价理论能够提高情感文本生成的准确性，并使用户可以实现更加精细的控制生成的文本。

通过细粒度评估条件情感自然语言生成事件描述

Affective Natural Language Generation of Event Descriptions through  Fine-grained Appraisal Conditions

Human use language not just to convey information but also to express their
inner feelings and mental states. In this work, we adapt the state-of-the-art
language generation models to generate affective (emotional) text. We posit a
model capable of generating affect-driven and topic-focused sentences without
losing grammatical correctness as the affect intensity increases. We propose to
incorporate emotion as prior for the probabilistic state-of-the-art text
generation model such as GPT-2. The model gives a user the flexibility to
control the category and intensity of emotion as well as the topic of the
generated text. Previous attempts at modelling fine-grained emotions fall out
on grammatical correctness at extreme intensities, but our model is resilient
to this and delivers robust results at all intensities. We conduct automated
evaluations and human studies to test the performance of our model and provide
a detailed comparison of the results with other models. In all evaluations, our
model outperforms existing affective text generation models.

本文介绍了一种使用情感驱动的文本生成模型，其能够有效地生成带有情感色彩的、主题集中的且语法正确的句子，并集成了 GPT-2 等概率文本生成模型。该模型考虑了情感类别、强度和主题的灵活性，并在自动化评估和人类研究中优于其他情感文本生成模型。