  While cultural backgrounds have been shown to affect linguistic expressions,
existing natural language processing (NLP) research on culture modeling is
overly coarse-grained and does not examine cultural differences among speakers
of the same language. To address this problem and augment NLP models with
cultural background features, we collect, annotate, manually validate, and
benchmark EnCBP, a finer-grained news-based cultural background prediction
dataset in English. Through language modeling (LM) evaluations and manual
analyses, we confirm that there are noticeable differences in linguistic
expressions among five English-speaking countries and across four states in the
US. Additionally, our evaluations on nine syntactic (CoNLL-2003), semantic
(PAWS-Wiki, QNLI, STS-B, and RTE), and psycholinguistic tasks (SST-5, SST-2,
Emotion, and Go-Emotions) show that, while introducing cultural background
information does not benefit the Go-Emotions task due to text domain conflicts,
it noticeably improves deep learning (DL) model performance on other tasks. Our
findings strongly support the importance of cultural background modeling to a
wide variety of NLP tasks and demonstrate the applicability of EnCBP in
culture-related research.
