Challenges and Strategies in Cross-Cultural NLP (2022)

Abstract: 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.

## a framework for cultural awareness language technology is rapidly advancing for a minority of the world's languages. at the same time, the majority of languages are falling behind @xcite . it is essential that language technology can serve the speakers of a wide variety of languages, who come from a wide variety of cultures. in this paper, we argue that doing so requires accommodating these speakers not only on a linguistic level, but also on a cultural level. culture, like language, is a term that is hard to pin down, but generally describes the way of life of a collective group of people, and distinguishes them from other groups with other cultures @xcite . culture encompasses both material as well as non-material aspects, such as beliefs and linguistic practices @xcite . moreover, since "[c]ulture is the acquired knowledge people use to interpret experience and generate behavior" @xcite , it is also the lens through which people understand linguistic messages. culturally maladapted messages can and will be misinterpreted; nlp must be culturally sensitive in order to avoid doing harm. language and culture interact in a number of ways (see figure 1 ). this paper aims to illuminate these connections, in order to motivate and inform future nlp work. beyond linguistic form and style, how things are expressed in language, they include common ground, the shared knowledge based on which people reason and communicate; aboutness, what information people care to convey; and objectives or values, the goals people strive for (e.g., when developing language technology). since language and culture are intertwined, the different dimensions may be difficult to tease apart @xcite : for example, the lexicon is shaped both by the need to convey information people care about, and by the conceptual categories grounded in the environment. style is related to values and societal structure: formality levels in japanese, for example, reflect the hierarchy of japanese society @xcite . nevertheless, language and culture are not interchangeable terms. culture varies greatly within languages @xcite . for example, the assumption that "english" in and of itself carries a single set of worldviews, interests and norms is unjustified @xcite . on the other hand, a relatively homogeneous culture can span multiple languages, as in the nordic countries @xcite . contributions. we propose a framework for understanding the challenges that cultural diversity poses for nlp to serve all users, as well as the opportunities that nlp creates to understand these differences better. we consider four elements: linguistic form, common ground, aboutness and values. we then survey existing strategies in nlp to address these challenges. highlighting limitations in current strategies, as well as successful examples, we propose directions for future development of cross-cultural nlp. ## linguistic form and style linguistic form refers to non-semantic questions of how to formulate an utterance. most work on crosslingual nlp has focused on how linguistic form varies between languages. however, the impact of social and cultural factors on linguistic form and stylistic variations is rarely discussed @xcite . ## common ground a culture is in part defined by a shared common ground: the shared body of knowledge that can be talked about and that can be assumed as known by others. this common ground varies from culture to culture, and thus cross-cultural language use has to take into account shifts in common ground. these cross-cultural shifts, often correlating with cross-lingual shifts, are neglected in nlp. an underlying assumption in many approaches to multilingual nlp is that "different languages share a similar semantic structure" @xcite . however, the assumption that there is a crosslingual, cross-culturally common semantics to preserve fails when the common grounding does not match between cultures. two relevant aspects here are the set of relevant concepts, closely identified with problems of lexicalisation, and common sense, i.e., the relevant propositional knowledge used in reasoning and entailment. conceptualisation. people carve up the world using conceptual categories @xcite . while some general or even universal patterns exist @xcite , these categories can and do differ between languages and cultures: the domain of colour is a famous example @xcite , but cross-cultural differences are also reflected in kinship systems (the lexicalization of family structures @xcite , spatial relations @xcite and basic objects (e.g., where to draw the distinction between a 'cup' and a 'mug', or the 'hand' and the 'arm'; @xcite . translating between languages thus entails translating between conceptualisations, which can be impossible if the conceptual grounding is not available (is this danish "kop" an english 'cup' or a 'mug?')-this is the motivation behind multimodal, i.e., visually grounded, translation @xcite . data collected from a single culture may contain concepts that do not easily map across cultures, as for example seen in the recent marvl dataset @xcite , in which about a quarter of concepts from a sample of non-western cultures did not map to english concepts (as represented by wordnet). commonsense knowledge. "common sense" is the knowledge that is held in common by a community and culture, the communal knowledge bank that can be presumed to be known by everyone. common sense thus covers a diverse set of knowledge types, from physical and temporal reasoning to psychological, social and moral judgements @xcite . some aspects of common sense are fairly universal, in particular those that arise from inhabiting a grounded human body on earth (dropping a glass of juice onto a hard surface is likely to cause a mess; the baby might be crying because it's hungry). on the other hand, axioms of social and moral common sense, for example the how and why of rituals such as marriages, vary between cultures @xcite . moving between different banks of common sense can involve different strategies to either provide missing background/common knowledge, or to transfer the content to a target-culture appropriate setting (see §6.3). ## aboutness cultures promote different topics and issues @xcite , sometimes by necessity, sometimes by accident. foot_0 some of the domains commonly considered in (english-speaking, anglo-western) nlp are irrelevant to some cultures, while others mean different things to different cultures, i.e., they involve completely different practices. an example of the former is beer reviews, a go-to domain in sentiment analysis @xcite @xcite . beer reviews are hardly meaningful in cultures with no beer consumption. an example of the latter, i.e., a domain that is used differently across cultures and social groups, is twitter (hine, 2020). as an example of multi-domain sentiment analysis, @xcite consider cameras, laptops, restaurants, and movies, within the context of data from u.s. american websites. these domains, however, apply differently in other cultures: restaurants are more important in some cultures (e.g., copenhagen hipsters) than others (e.g., sámi); similarly, laptops have not penetrated cultures equally and have in fact been found to change culture @xcite . beyond conceptualisation differences (see §3), @xcite point out how the visual concepts at the core of many multimodal nlp tasks reflect a northern american and western european bias in the underlying data sources, from which images were scraped. as an alternative, in their new dataset (marvl), they let the selection of both concepts and images be entirely driven by native speakers. this corrects for some existing asymmetries in what our evaluation data tends to be about. for example, @xcite created a dataset entirely devoted to pizzas; in @xcite , models' ability to recognise vadas, an indian food, is evaluated. people describe the same events differently across cultures. news reports emphasise different goals, motivations, methods and content in different parts of the world @xcite @xcite . however, many news resources used in nlp research reflect only some cultures @xcite @xcite . as nlp technologies have been widely adopted to build multilingual news generators @xcite @xcite , it is important that these differences are taken into account in language generation. ## objectives and values common objectives in nlp include progress, accuracy, fairness, robustness and interpretability. they are driven by the research community, and reflect the values of this community: novelty, for example, is highly esteemed, which is not a value held by more conservative, tradition-valuing cultures. grounding these objectives explicitly in ethical values and norms, and acknowledging that these may differ across cultures, is essential for crosscultural nlp. @xcite , for example, introduce commonsense norm bank and delphi, a moral reasoning resource and model, respectively, reflecting "english-speaking cultures of the united states in the 21st century". however, researchers, practitioners, regulators and users may belong to different cultures and have different objectives and values @xcite . another common goal in the nlp community is the eventual expansion of all nlp technologies to low-resource languages. however, as bird (2020) points out, this goal must not overshadow, or be placed above, the specific desires and needs of a given language community, as every community will have their own objectives for what they do and do not want for their language with regards to technology. without building relationships with the communities for which we aim to develop nlp, there is a serious risk for imposing our own objectives on the community. importantly, entire categories of applications and tasks may be driven by cultural assumptions. for example, the main objective of text summarisation is brevity @xcite . likewise, while fluency is a common objective of machine translation, it may in fact, be less important than comprehensibility for users @xcite . values differ across cultures. values are an important part of non-materialistic culture, as these values define what a specific culture deems to be good or valuable, as opposed to bad and undesirable @xcite . accordingly, these values help inform what is accepted as normal and ethical behaviour, and shape common cultural attitudes. aspects that clash against a culture's values, norms, and ethics, may often be taboo or even illegal. as cultures around the world hold different values, their norms and ethics are inevitably different, too @xcite . for example, in some cultures, alcohol consumption is prohibited for religious reasons, and thus may not be normal or acceptable behaviour; meanwhile drinking alcohol can be seen as normal behaviour by some cultures without this shared value. for this reason, beer reviews in an application, for example, may be seen not only as irrelevant (see §4), but even as offensive. while these values can and do change greatly from culture to culture, end-users of nlp systems deserve technologies that are suitable to the culture that they belong to. to this end, the preservation of cultural values, norms, and ethics in models intended for users from different cultures, is another important aspect of cross-cultural nlp @xcite . fairness and combating biases. many recent studies in nlp have explored strategies to counteract unwanted biases (according to the community's values, see above) in models, resulting from prejudices present in society. these biases are implicit in the data used to train our models, and become baked into the models themselves @xcite @xcite @xcite . because nlp datasets are composed of utterances from members of a larger culture, the resulting biases constitute a partial reflection of that culture's values, norms, and ethics. for example, work in nlp on gender bias demonstrate that there is a pervasive problem with sexism in models (a direct result of sexist attitudes present in society), and explore ways to remedy this @xcite @xcite . accordingly, just as members of any culture deserve language technology that truly serves them, members of vulnerable groups and marginalised members in larger cultures deserve such as well. current research in addressing fairness and combating biases in models demonstrates how nlp can be used to tackle the challenge of inequitable cultural attitudes. in other words, the goal of countering bias in nlp can be seen as shifting existing cultures into hopefully more equitable ones. ## strategies we have identified several challenges along four dimensions of culture. here, we highlight general strategies for cross-cultural nlp. we identify three main areas where we could direct efforts towards mitigating cross-cultural disparities: data collection, model training, and translation. data is the backbone of nlp and any efforts towards cross-cultural nlp needs to consider the strategies involved in collecting and annotating it. transfer learning is central to cross-lingual nlp and can serve an important role in cross-cultural nlp. finally, translation is used to communicate between languages and will often be necessary when communicating between cultures. we explain what can be done within each of these areas each in turn. ## conclusion in this paper, we have touched on a number of ways in which cultural knowledge, preferences and values can affect nlp practices. it is important to acknowledge the breadth of culture in all of these aspects, if we should aspire to a more cross-cultural nlp. however, we do not pretend to cover all relevant aspects of culture in our taxonomy: many works in sociology posture frameworks to better explain and catalogue the specific elements that compose a culture @xcite @xcite . we encourage further investigation of their impact on nlp. finally, present-day computational science have inherited colonising practices, which in nlp are realised as "homogenisation of perspectives" and "algorithmic monoculture" @xcite . while there is general agreement in the nlp community that we need to represent cultures from outside western, educated, industrialised, rich, and democratic (weird) societies @xcite , representation alone is not enough if we do not also allow them to prioritise their goals and values, rather than the goals and values held by the nlp community. decolonisation @xcite @xcite aims to dismantle harmful power asymmetries and concepts of knowledge, turning us instead towards a "pluriversal epistemology of the future" (mignolo, 2012) that unlike universalisms, acknowledges and supports a wider radius of socio-political, ecological, cultural, and economic needs @xcite .