论文标题
学习情感含义，使用来自变形金刚的双向编码器表示社会行为
Learning affective meanings that derives the social behavior using Bidirectional Encoder Representations from Transformers
论文作者
论文摘要
预测过程的结果需要对系统动态进行建模并观察状态。在社会行为的背景下，情感是系统的状态。影响控制理论（ACT）使用情感来表现潜在的相互作用。 ACT是基于三维情感词典的文化和行为的生成理论。传统上，使用调查数据对情感进行量化，该数据被送入回归模型以解释社会行为。调查中使用的词典由于成本过高而受到限制。本文使用来自变压器（BERT）模型的微调双向编码器表示，以开发这些调查的替代品。该模型在估计情感含义，扩展情感词典并允许解释更多行为方面达到了最新的准确性。
Predicting the outcome of a process requires modeling the system dynamic and observing the states. In the context of social behaviors, sentiments characterize the states of the system. Affect Control Theory (ACT) uses sentiments to manifest potential interaction. ACT is a generative theory of culture and behavior based on a three-dimensional sentiment lexicon. Traditionally, the sentiments are quantified using survey data which is fed into a regression model to explain social behavior. The lexicons used in the survey are limited due to prohibitive cost. This paper uses a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model to develop a replacement for these surveys. This model achieves state-of-the-art accuracy in estimating affective meanings, expanding the affective lexicon, and allowing more behaviors to be explained.
