  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.
