Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks

TL;DR

This paper introduces pragmatic linguistic features capturing cross-cultural similarities to improve transfer learning for sentiment analysis across languages, showing these features outperform traditional typological measures.

Contribution

It proposes three novel pragmatic features for cross-cultural similarity, demonstrating their effectiveness in enhancing cross-lingual transfer learning for sentiment analysis.

Findings

Pragmatic features align with sociolinguistic theories.

Pragmatic features improve transfer language selection.

Features capture cultural nuances in language use.

Abstract

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.