Before revision: Cross-lingual transfer learning studies how datasets, annotations, and models can be transferred from resource-rich languagesto improve language technologies in resource-poor settings. Recent works have shown that we can further benefit from the selection of the best transfer language. In this paper, we propose three pragmatically-motivated features that can help guide the optimal transfer language selection problem for cross-lingual transfer. Specifically, the proposed features operationalize cross-cultural similarities that manifest in various linguistic patterns : language context-level, sharing multi-word expressions , and the use of emotion concepts.
After revision: Much work in cross-lingual transfer learning explored how to select better transfer languages for multilingual tasks, primarily focusing on typological and genealogical similarities between languages. We hypothesize that these measures of linguistic proximity are not enough when working with pragmatically-motivated tasks, such as sentiment analysis. As an alternative, we introduce three linguistic features that capture cross-cultural similarities that manifest in various linguistic patterns : language context-level, sharing multi-word expressions , and the use of emotion concepts.