Challenges and Strategies in Cross-Cultural NLP (2203.10020v1)

Abstract: Various efforts in the NLP community have been made to accommodate linguistic diversity and serve speakers of many different languages. However, it is important to acknowledge that speakers and the content they produce and require, vary not just by language, but also by culture. Although language and culture are tightly linked, there are important differences. Analogous to cross-lingual and multilingual NLP, cross-cultural and multicultural NLP considers these differences in order to better serve users of NLP systems. We propose a principled framework to frame these efforts, and survey existing and potential strategies.

• The paper introduces a framework incorporating cultural dimensions such as linguistic style, common ground, aboutness, and values to enhance NLP.
• It presents actionable strategies like diverse data collection, transfer learning, and culturally adaptive translation to mitigate biases.
• The study emphasizes the ethical importance of participatory design and decolonization for creating equitable and culturally-aware NLP systems.

Challenges and Strategies in Cross-Cultural NLP

The paper "Challenges and Strategies in Cross-Cultural NLP" offers a comprehensive analysis of the integration of cultural diversity into NLP. While linguistic diversity has been explored extensively within multilingual and cross-lingual NLP, this paper emphasizes the importance of cultural considerations, presenting a framework for understanding the interplay between language and culture. The authors delineate four dimensions potentially affected by cultural biases in NLP: linguistic form and style, common ground, aboutness, and objectives/values.

Framework for Cultural Awareness

The framework proposed by the authors reflects a need within the NLP community to shift from a solely linguistic focus to one encompassing cultural variables. This approach acknowledges that language and culture, although interconnected, represent distinct constructs affecting the interpretation and generation of linguistic messages. Theoretically, culturally-sensitive NLP is posited to prevent misinterpretation and potential harm in communication, given the distinct ways culture shapes language.

Dimensions of Culture in NLP

• Linguistic Form and Style: Variations in linguistic form and stylistic choices across cultures are highlighted as sources for potential biases in NLP systems. The paper provides examples where pre-trained LLMs do not equally represent sociolects within a language, thus privileging dominant cultural narratives. Stylistic variations across cultures are discussed in terms of politeness, emotion expression, and pragmatic failures.
• Common Ground: NLP must account for cross-cultural differences in common ground, or shared knowledge, which varies between cultural groups. Assumptions about common semantic structures across languages can lead to discrepancies in reasoning or entailment when cultural common sense diverges.
• Aboutness: The cultural relevance of topics frequently analyzed in NLP is emphasized as potentially skewed towards Western interests. This dimension identifies cultural biases in datasets, calling for culturally-inclusive domain selections in tasks such as sentiment analysis.
• Objectives and Values: The authors address conflicting objectives within the field of cross-cultural NLP. While multicultural pluralism and societal equity are both desired, they may compete when preserving cultural values conflicts with reducing harmful cultural biases in NLP outputs.

Strategies for Addressing Cross-Cultural Disparities

The paper identifies three principal areas where researchers could direct efforts to reduce cultural biases in NLP: data collection, model training, and translation.

• Data Collection: The paper suggests diversifying data sources, engaging culturally-varied annotators, and addressing discrepancies in dataset annotations as vital measures. Annotation projection is acknowledged as a method to leverage existing resources across languages but is critiqued for potentially ignoring cultural specificity.
• Model Training: Approaches such as transfer learning and pre-training in multilingual frameworks are analyzed for their potential role in improving cross-cultural representation. Training strategies like Distributionally Robust Optimization are noted for focusing on minority performance, offering pathways to scenario-specific equity.
• Translation: Translation across cultures must adapt to cultural contexts, sometimes deviating from direct translation principles. Style transfer within a language can serve to modify textual content according to cultural norms, but evaluation metrics for such adaptations require further development.

Implications and Future Directions

Practically, incorporating cultural awareness into NLP systems has implications for developing technology that responsibly serves diverse user needs and communicates appropriately across cultural boundaries. The authors emphasize the ethical considerations inherent in NLP work, advocating for participatory design that respects local cultural sovereignty and avoids NLP colonization. The paper concludes by urging a conscious effort towards decolonization within computational science, recognizing the need to dismantle homogenizing practices in favor of culturally pluralistic approaches.

Overall, this paper provides a framework that may guide future research into culturally-aware NLP systems, advocating for significant theoretical reflections in developing equitable and culturally-centered NLP technologies.