Traditional methods of personality assessment, and survey-based research in general, cannot make inferences about new items that have not been surveyed previously. This limits the amount of information that can be obtained from a given survey. In this article, we tackle this problem by leveraging recent advances in statistical natural language processing. Specifically, we extract "embedding" representations of questionnaire items from deep neural networks, trained on large-scale English language data. These embeddings allow us to construct a high-dimensional space of items, in which linguistically similar items are located near each other. We combine item embeddings with machine learning algorithms to extrapolate participant ratings of personality items to completely new items that have not been rated by any participants. The accuracy of our approach is on par with incentivized human judges given an identical task, indicating that it predicts ratings of new personality items as accurately as people do. Our approach is also capable of identifying psychological constructs associated with questionnaire items and can accurately cluster items into their constructs based only on their language content. Overall, our results show how representations of linguistic personality descriptors obtained from deep language models can be used to model and predict a large variety of traits, scales, and constructs. In doing so, they showcase a new scalable and cost-effective method for psychological measurement. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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人格评估的深度学习方法：跨项目概括并扩大基于调查的研究范围。

传统的人格评估方法和一般的基于调查的研究无法对以前未调查过的新项目做出推断。这限制了从给定调查中可以获得的信息量。在本文中，我们通过利用统计自然语言处理的最新进展来解决这个问题。具体来说，我们从深度神经网络中提取问卷项目的“嵌入”表示，并在大规模英语语言数据上进行训练。这些嵌入使我们能够构建一个高维的项目空间，其中语言相似的项目彼此靠近。我们将项目嵌入与机器学习算法相结合，将参与者对个性项目的评分推断为尚未被任何参与者评分的全新项目。我们的方法的准确性与执行相同任务的激励人类法官相当，这表明它可以像人类一样准确地预测新个性项目的评级。我们的方法还能够识别与问卷项目相关的心理结构，并且可以仅根据项目的语言内容准确地将项目聚类到其结构中。总的来说，我们的结果展示了如何使用从深层语言模型获得的语言个性描述符的表示来建模和预测各种各样的特征、尺度和结构。在此过程中，他们展示了一种新的可扩展且具有成本效益的心理测量方法。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。