Adapting Entities across Languages and Cultures (2021)

Abstract: How would you explain Bill Gates to a German? He is associated with founding a company in the United States, so perhaps the German founder Carl Benz could stand in for Gates in those contexts. This type of translation is called adaptation in the translation community. Until now, this task has not been done computationally. Automatic adaptation could be used in natural language processing for machine translation and indirectly for generating new question answering datasets and education. We propose two automatic methods and compare them to human results for this novel NLP task. First, a structured knowledge base adapts named entities using their shared properties. Second, vector-arithmetic and orthogonal embedding mappings methods identify better candidates, but at the expense of interpretable features. We evaluate our methods through a new dataset of human adaptations.

## when translation misses the mark imagine reading a translation from german, "i saw merkel eating a berliner from dietsch on the ice". this sentence is opaque without cultural context. an extreme cultural adaptation for an american audience could render the sentence as "i saw biden eating a boston cream from dunkin' donuts on the acela", elucidating that merkel is in a similar political post to biden; that dietsch (like dunkin' donuts) is a mid-range purveyor of baked goods; both berliners and boston creams are filled, sweet pastries named after a city; and ice and acela are slightly ritzier high-speed trains. human translators make this adaptation when it is appropriate to the translation @xcite . because adaptation is understudied, we leave the full translation task to future work. instead, we focus on the task of cultural adaptation of entities: given an entity in a source, what is the corresponding entity in english? most americans would not recognize christian drosten, but the most efficient explanation to an american would be to say that he is the "german anthony fauci" @xcite . we provide top adaptations suggested by algorithms and humans for another american involved with the pandemic response, bill gates, in can machines reliably find these analogs with minimal supervision? we generate these adaptations with structured knowledge bases (section 3) and word embeddings (section 4). we elicit human adaptations (section 5) to evaluate whether our automatic adaptations are plausible (section 5.3). we define cultural adaptation and motivate its application for tasks like creating culturally-centered training data for qa. @xcite define adaptation as translation in which the relationship not the literal meaning between the receiver and the content needs to be recreated. you could formulate our task as a tradi-tional analogy drosten::germany as fauci::united states @xcite , but despite this superficial resemblance (explored in section 4), traditional approaches to analogy ignore the influence of culture and are typically within a language. hence, analogies are tightly bound with culture; humans struggle with analogies outside their culture @xcite . we can use this task to identify named entities @xcite @xcite and for understanding other cultures @xcite . ## adaptation from a knowledge base we first adapt entities with a knowledge base. we use wikidata @xcite , a structured, human-annotated representation of wikipedia entities that is actively developed. this resource is well-suited to the task as features are standardized both within and across languages. many knowledge bases explicitly encode the nationality of individuals, places, and creative works. entities in the knowledge base are a discrete sparse vector, where most dimensions are unknown or not applicable (e.g., a building does not have a spouse). for example, angela merkel is a human (instance of), german (country of citizenship), politician (occupation), rotarian (member of), lutheran (religion), 1.65 meters tall (height), and has a phd (academic degree). how would we find the "most similar" american adaptation to angela merkel? intuitively, we should find someone whose nationality is american. some issues immediately present themselves; contemporary entities will have more non-zero entries than older entities. some characteristics are more important than others: matching unique attributes like "worked as journalist" is more important than matching "is human". each entity in wikidata has "properties", which we can think about as the dimension of a sparse vector and "values" that those properties can take on. for example, merkel has the properties "occupation" and "academic degree". values for those properties are that her "occupation" is "politician" and her "academic degree" is a "doctorate". to match entities across cultures, we focus on matching properties rather than values; many of the values are more relevant inside a culture. for example, we cannot find american politicians who belong to the christian democratic union, but we can find politicians who have an academic degree and a dissertation title. as a toy example, if beethoven, merkel, and bach all have only two properties: beethoven has an "occupation" and "genre", merkel has an "erdős number" and "political party", and bach has a "occupation" and "genre", then beethoven and bach has a distance of zero and are the closest entities while merkel has a distance of two since {"erdős number", "political party"} is two away from {"occupation", "genre"}. first, we bifurcate wikidata into two sets: an american set a for items which contain the value "united states of america" and a german set d for those with german values. 3 this is a liberal approximation, but it successfully excludes roughly seven out of the eight million items in wikidata. then we explore the properties from wikidata. we create entity vectors with dimensions corresponding to frequently-occurring properties. the properties are discrete and categorical; merkel either has an "occupation" or she does not. each entity then has a sparse vector. we calculate the similarity of the vectors with faiss's l 2 distance @xcite and for each vector in a find the closest vector in d and vice versa. so who is the american angela merkel? one possible answer is woodrow wilson, a member of a "political party", who had a "doctoral advisor" and a "religion", and ended up with "awards". this answer may be unsatisfying as it was barack obama who sat across from merkel for nearly a decade. to capture these more nuanced similarities, we turn to large text corpora in section 4. ## an alternate embedding approach while the classic nlp vector example @xcite isn't as magical as initially claimed @xcite , it provides useful intuition. we can use the intuitions of the cliché: to adapt between languages. this, however, requires relevant embeddings. first, we use the entire wikipedia in english and german, preprocessed using moses @xcite . we follow @xcite and use named entity recognition @xcite to tokenize entities such as barack_obama. we use word2vec @xcite , rather than fasttext @xcite , as we do not want orthography to influence the similarity of entities. angela merkel in english and in german have quite different neighbors, and we intend to keep it that way by preserving the distinction between languages. however, the standard word2vec model assumes a single monolingual embedding space. we use unsupervised vecmap @xcite , a leading tool for creating cross-lingual word embeddings, to build bilingual word embeddings. we propose two approaches for adaptation. learned adaptation to eliminate the need for manual anchor selection for both cultures, our second approach learns the adaptation as a linear transformation of source embeddings to the target culture given a few adaptation examples. specifically, we use the human adaptations sourced for the wikipedia entities as training for the veale noc ones. we follow the work of @xcite and learn a transformation matrix w @xmath0 likewise, we learn the reverse mapping w @xmath1. this requires supervised training data-but not much @xcite )-which we collect in section 5. ## comparing automation to human judgment the automated methods can generate entities at scale, but humans have to evaluate their relevance. ## a new computational task we formally introduce entity adaptation as a new computational task. word2vec embeddings and wikidata can be used to figuratively-not just literally-translate entities into a different culture. humans are better at generating candidates for this task than our computational methods (figure 1 ). these methods are well-motivated, but have room for improvement. knowledge bases improve over time and increased coverage of entities-as well as improved information about each entity-would improve the method. alternate word embedding approaches-perhaps those that discard orthography-may provide better candidates. even humans occasionally disagree with other humans on this task, so evaluation for this task is nontrivial. our new dataset of machine-generated adaptations, human adaptations, and human evaluation of these adaptations can serve as an evaluation for future automatic methods. people need nlp systems that reflect their language and culture, but datasets are lacking: adaptation can help. there has been an explosion of english-language qa datasets, but other languages continue to lag behind. several approaches try to transfer english's bounty to other languages @xcite , but most of the entities asked about in major qa datasets are american @xcite . adapting entire questions will require not just adapting entities and non-entities in tandem but will also require integration with machine translation @xcite . our automatic methods did not create precise adaptations, but the alternative "incorrect" adaptations may be useful for low-precision tasks, such as generating numerous simple open-ended questions or gauging the popularity of a entity. given the existence of robust datasets in high resource languages can we adapt, rather than literally translate, them to other cultures and languages?