We propose a multilingual model to recognize Big Five Personality traits from text data in four different languages: English, Spanish, Dutch and Italian. Our analysis shows that words having a similar semantic meaning in different languages do not necessarily correspond to the same personality traits. Therefore, we propose a personality alignment method, GlobalTrait, which has a mapping for each trait from the source language to the target language (English), such that words that correlate positively to each trait are close together in the multilingual vector space. Using these aligned embeddings for training, we can transfer personality related training features from high-resource languages such as English to other low-resource languages, and get better multilingual results, when compared to using simple monolingual and unaligned multilingual embeddings. We achieve an average F-score increase (across all three languages except English) from 65 to 73.4 (+8.4), when comparing our monolingual model to multilingual using CNN with personality aligned embeddings. We also show relatively good performance in the regression tasks, and better classification results when evaluating our model on a separate Chinese dataset.

本文提出了一种多语言模型，用于从四种不同语言（英语、西班牙语、荷兰语和意大利语）的文本数据中识别大五人格特质。我们提出了一种任务对齐方法（GlobalTrait），将训练特征从英语这样的高资源语言传递到其他低资源语言中，我们通过使用卷积神经网络（CNN）和人格对齐后的多语言嵌入，将平均F-score从65提高到73.4（+8.4），在分类任务中取得更好的结果，同时在回归任务中也表现出较好的性能。

全球特质: 多语言词嵌入的个性化调整