Cross-lingual transfer learning studies how datasets, annotations, and models can be transferred from resource-rich languages to 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. Our experimental results show that these features significantly improve the prediction of optimal transfer languages over baselines in sentiment analysis, but are less useful for dependency parsing. Further analyses show that the proposed features indeed capture the intended cross-cultural similarities and align well with existing work in sociolinguistics and linguistic anthropology.

本研究通过引入三种语言特征，即语言背景水平、比喻语言和情感概念的词汇化，捕捉语言语用学中的跨文化相似性并证实其在情感分析等实际任务中的有效性。

跨文化相似特征在语用动机任务跨语言迁移学习中的应用