论文标题
适应受控情感文本生成的语言模型
Adapting a Language Model for Controlled Affective Text Generation
论文作者
论文摘要
人类使用语言不仅是为了传达信息,还可以表达其内在的感受和精神状态。在这项工作中,我们适应了最先进的语言生成模型,以产生情感(情感)文本。我们提出了一个模型,能够生成情感驱动和以主题为中心的句子而不会随着情感强度的增加而失去语法正确性。我们建议将情感纳入概率最先进的文本生成模型(例如GPT-2)的先验。该模型使用户可以灵活地控制情感的类别和强度以及生成的文本主题。以前的尝试对细颗粒情绪进行建模的尝试在极端强度的语法正确性上脱颖而出,但是我们的模型对此具有弹性,并在所有强度上都提供了强大的结果。我们进行自动评估和人类研究,以测试模型的性能,并与其他模型进行详细比较。在所有评估中,我们的模型都优于现有的情感文本生成模型。
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.