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Challenges and Strategies in Cross-Cultural NLP
Daniel Hershcovich, Stella Frank, Heather Lent, Miryam de Lhoneux, Mostafa Abdou, Stephanie Brandl, Emanuele Bugliarello, Laura Cabello Piqueras, Ilias Chalkidis, Ruixiang Cui, Constanza Fierro, Katerina Margatina, Phillip Rust, Anders Søgaard
TL;DR
NLP has expanded across languages, but serving users well also requires accounting for cultural variation in language, knowledge, interests, and values. This paper proposes a four-part framework, surveys strategies across data, training, and translation, and identifies representation disparities and ethical boundaries requiring participatory approaches.
Problem
NLP efforts serving multiple languages do not guarantee cultural representation, because data sources, annotations, models, and evaluation can privilege some cultures.
Method
The paper proposes a framework covering linguistic form, common ground, aboutness, and values, then surveys strategies in data collection, model training, and translation.
Results
The survey identifies representation disparities in data and models, culturally varying commonsense knowledge, and automatically translated data that can perform worse than native target-language data.
Takeaways & Limitations
Cross-cultural NLP requires diverse sources and annotators, culturally sensitive evaluation, and adaptation methods that account for differences within and across languages.
Takeaways & Limitations
Cross-cultural NLP must address unresolved conflicts between researchers’ ethical values and multicultural acceptance, while involving local communities participatively to respect data sovereignty.
Abstract
from arXiv · showhide
Various efforts in the Natural Language Processing (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.
1 A Framework for Cultural Awareness
Cross-cultural NLP must account for cultural differences alongside linguistic diversity because language technologies can misinterpret culturally shaped messages. The paper frames these differences through four dimensions and surveys strategies for addressing them.
- Language technology must serve speakers across diverse languages and cultures, not accommodate linguistic variation alone.
- Culture shapes how people interpret linguistic messages, so culturally maladapted NLP outputs can be misinterpreted and cause harm.
- The framework identifies four dimensions of cultural variation: linguistic form and style, common ground, aboutness, and objectives or values.
- Language and culture are intertwined but not interchangeable: cultures can vary within one language, while one relatively homogeneous culture can span multiple languages.
- The paper proposes a framework, surveys existing strategies, highlights their limitations and successes, and develops directions for future cross-cultural NLP.
2 Linguistic Form and Style
Cultural variation affects linguistic form, style, dialects, sociolects, politeness, directness, and emotional expression within and across languages. Treating languages as homogeneous can limit adaptation and privilege some cultural groups.
- Linguistic form concerns how utterances are formulated, while cultural and social influences on form and stylistic variation remain rarely discussed.
- Variation within language: Languages vary internally across countries, dialects, and social groups, with dialects serving as important signs of cultural identity.
- Variation within language: Treating a language as homogeneous limits cultural adaptation and can privilege some cultures over others.
- Variation within language: A dialect-specific Arabic pretrained model outperforms multilingual or general Arabic models in sentiment classification.
- Stylistic variation: The same communicative intention can take culturally different forms, so directness or politeness may be appropriate in one culture but offensive in another.
- Stylistic variation: Emotional expression differs across cultures and matters for cross-cultural sentiment analysis and text-based recognition of conditions such as depression.
3 Common Ground
Cross-cultural language use depends on common ground, which varies across cultures and is neglected by assumptions of shared semantics. Differences in concepts and commonsense knowledge require adaptation strategies.
- Common ground is shared cultural knowledge that can be assumed in communication, but it shifts across cultures.
- NLP often neglects cross-cultural shifts by assuming that different languages share similar semantic structure.
- Cultural differences in common ground affect relevant concepts and the propositional knowledge used for reasoning and entailment.
- Commonsense knowledge: Commonsense knowledge includes physical, temporal, psychological, social, and moral judgments, with social and moral norms varying across cultures.
- Cross-cultural systems can address differing commonsense banks by supplying missing background knowledge or transferring content into a target-culture setting.
4 Aboutness
Cultures differ in the topics they value and in how they describe events, while NLP data and tasks often reflect Anglo-Western priorities. Culturally grounded data collection and generation can broaden what systems represent.
- Cultures promote different topics, making some common NLP domains irrelevant or differently meaningful across communities.
- Many multimodal NLP tasks inherit Northern American and Western European bias from the visual concepts and images in their source data.
- Group membership shapes interests and makes some topics vital through conditions such as dependence on weather, agriculture, or livestock.
- Native-speaker-driven selection of concepts and images, as in MarVL, addresses asymmetries in evaluation data aboutness.
- News reports emphasize different goals, motivations, methods, and content across regions, but many NLP news resources represent only some cultures.
5 Objectives and Values
NLP objectives and values are culturally situated: goals such as expansion, brevity, fluency, accuracy, and fairness may not align across communities. Cross-cultural NLP must therefore balance respect for cultural differences with protection against inequitable attitudes.
- NLP objectives reflect research-community values, so cross-cultural work should ground goals such as accuracy, fairness, and robustness in differing ethical norms.
- NLP’s goal of expanding technology to low-resource languages must not override each community’s own technological desires and needs.
- Applications can embody cultural assumptions: summarization commonly prioritizes brevity, while some translation users may value comprehensibility over fluency.
- Cultural values shape what communities regard as normal, ethical, acceptable, taboo, or illegal.
- Cross-cultural NLP must navigate a tension between representing diverse cultural values and addressing inequitable attitudes that harm users.
- No discussed strategy fully resolves the risk of either imposing one culture’s values or leaving marginalized groups vulnerable to bias.
6 Strategies
Cross-cultural NLP strategies target disparities in data, model training, and translation across four cultural dimensions. The paper emphasizes culturally diverse representation, adaptation, and evaluation while identifying practical and methodological limits.
- Data collection: Data strategies should represent diverse sources and annotation perspectives because multilingual data alone does not guarantee cross-cultural representation.Representation disparity can arise from both data sources and annotation origins.
- Data collection: Geodiverse data collection and open communities broaden representation, but culture-specific training data remains costly and difficult to scale.Examples include geodiverse annotators, Universal Dependencies, and MasakhaneNER.
- Data collection: Annotation projection and machine translation expand low-resource data, but may ignore target-culture complexity or impose source-culture concepts.Automatically filtering social keywords is unlikely to capture all social behavior or cultural over-specificity.
- Translation: Culturally appropriate evaluation requires target-language annotation or sensitive human translation because automatic translation can introduce undetected cultural bias.Human translation or original target-culture data is expensive, while translating only high-quality evaluation data offers a middle-ground option.
- Model training: Pretraining encodes culture-specific differences, while group-aware training may improve parity but can require demographic attributes that only partially reflect culture.Group DRO has no benefit in one reported low-resource Creole setting, and fairness methods can also apply within a single language.
- Model training: Cross-lingual countermeasures remain culture-agnostic when languages are balanced without balancing their sources and cultures.The paper proposes targeting representation directly in terms of cultures rather than languages.
- Model training: Cultural-group granularity requires balancing generalisation against adaptation, with finer-grained representation potentially raising privacy concerns.The continuum ranges from representing individuals separately to treating large culturally diverse groups as homogeneous.
- Translation: Cross-cultural translation evaluation is difficult because style transfer lacks standardized protocols and reference-based metrics assume too few correct translations.The paper stresses culture-sensitive human evaluation.
7 Conclusion
The paper argues that cross-cultural NLP must account for cultural knowledge, preferences, and values, while acknowledging that its taxonomy does not cover every relevant aspect of culture. It also situates this work against homogenisation and algorithmic monoculture.
- Conclusion: The taxonomy highlights cultural breadth but does not pretend to cover all relevant aspects of culture.The paper notes that sociology offers additional frameworks for explaining and cataloguing cultural elements.
- Conclusion: Cross-cultural NLP must address homogenisation of perspectives and algorithmic monoculture, not merely represent cultures outside WEIRD societies.The paper argues that represented groups must also be able to prioritize their own goals and values.
- Conclusion: Representation alone is insufficient when NLP systems privilege the goals and values of the NLP community over those of represented cultures.This concern is framed as part of decolonisation efforts.
8 Ethical Aspects and Broader Impact
Cross-cultural NLP must confront value conflicts and involve local communities in shaping language technology and governing their data.
- Ethical conflicts: Norms and ethics vary across cultures, creating conflicts between researchers’ ethical values and multicultural acceptance.The paper does not resolve these conflicts, but argues that identifying which values should prevail in each case is essential.
- Community participation: Language technology for local communities should involve community members actively and participatorily.The paper connects this involvement to decolonising language technology and respecting local sovereignty over data.
- Open ethical questions: Cross-cultural NLP must ask which values should prevail when ethical commitments conflict with multicultural acceptance.