Telling stories is an integral part of human communication which can evoke
emotions and influence the affective states of the audience. Automatically
modeling emotional trajectories in stories has thus attracted considerable
scholarly interest. However, as most existing works have been limited to
unsupervised dictionary-based approaches, there is no benchmark for this task.
We address this gap by introducing continuous valence and arousal labels for an
existing dataset of children's stories originally annotated with discrete
emotion categories. We collect additional annotations for this data and map the
categorical labels to the continuous valence and arousal space. For predicting
the thus obtained emotionality signals, we fine-tune a DeBERTa model and
improve upon this baseline via a weakly supervised learning approach. The best
configuration achieves a Concordance Correlation Coefficient (CCC) of $.8221$
for valence and $.7125$ for arousal on the test set, demonstrating the efficacy
of our proposed approach. A detailed analysis shows the extent to which the
results vary depending on factors such as the author, the individual story, or
the section within the story. In addition, we uncover the weaknesses of our
approach by investigating examples that prove to be difficult to predict.

通过研究连续情感评价模型，我们收集、分析了儿童故事中的情感内容，提出了一种弱监督学习的方法来预测故事情感变化的连续值，通过 DeBERTa 模型改进，达到了较高的相关性和有效性。

利用 Transformer 和弱监督学习建模书面故事中的情感轨迹

Modeling Emotional Trajectories in Written Stories Utilizing  Transformers and Weakly-Supervised Learning

Large Language Models (LLMs) have shown a tremendous capacity for generating
literary text. However, their effectiveness in generating children's stories
has yet to be thoroughly examined. In this study, we evaluate the
trustworthiness of children's stories generated by LLMs using various measures,
and we compare and contrast our results with both old and new children's
stories to better assess their significance. Our findings suggest that LLMs
still struggle to generate children's stories at the level of quality and
nuance found in actual stories

大语言模型（LLMs）在生成文学文本方面显示出巨大的能力。然而，它们在生成儿童故事方面的有效性尚未得到全面的考查。在本研究中，我们使用各种指标评估 LLMs 生成的儿童故事的可靠性，并将我们的结果与旧的和新的儿童故事进行对比，以更好地评估它们的重要性。我们的研究结果表明，LLMs 在生成儿童故事方面仍然存在质量和细微之处的困难。