Story Commonsense

该数据集包含了11,129对情感句子，用于训练基于BERT的模型，旨在从句子中识别和提取情感。此外，该数据集还用于提升一个受控故事生成框架的效能，方法是将情感关键词作为输入。其任务是对叙事生成进行情感预测。

This dataset consists of 11,129 pairs of sentiment sentences, which is used to train BERT-based models for identifying and extracting sentiment from sentences. Moreover, this dataset is employed to enhance the performance of a controlled story generation framework by taking sentiment keywords as inputs, with the core task of conducting sentiment prediction for narrative generation.