Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks
This work addresses the challenge of cross-lingual transfer learning for pragmatically motivated tasks like sentiment analysis, offering a novel approach that could enhance performance in multilingual NLP applications.
The paper tackled the problem of selecting transfer languages for cross-lingual sentiment analysis by introducing pragmatic features based on cross-cultural similarities, and found that these features effectively improved transfer language choice compared to traditional typological and genealogical measures.
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 linguistic patterns and quantify distinct aspects of language pragmatics: language context-level, figurative language, and the lexification of emotion concepts. Our analyses show that the proposed pragmatic features do capture cross-cultural similarities and align well with existing work in sociolinguistics and linguistic anthropology. We further corroborate the effectiveness of pragmatically-driven transfer in the downstream task of choosing transfer languages for cross-lingual sentiment analysis.