War and Pieces: Comparing Perspectives About World War I and II Across W ikipedia Language Communities (2022)

Abstract: Wikipedia is widely used to train models for various tasks including semantic association, text generation, and translation. These tasks typically involve aligning and using text from multiple language editions, with the assumption that all versions of the article present the same content. But this assumption may not hold. We introduce a methodology for approximating the extent to which narratives of conflict may diverge in this scenario, focusing on articles about World War I and II battles written by Wikipedia’s communities of editors across four language editions. For simplicity, our unit of analysis representing each language communities’ perspectives is based on national entities and their subject-object-relation context, identified using named entity recognition and open-domain information extraction. Using a vector representation of these tuples, we evaluate how similarly different language editions portray how and how often these entities are mentioned in articles. Our results indicate that (1) language editions tend to reference associated countries more and (2) how much one language edition’s depiction overlaps with all others varies.

## introduction wikipedia's expansive content and multiple language editions have made it an invaluable resource, particularly for the training of large language models and translation models in natural language processing (nlp). less work has gone into quantifying the differences among language editions though. in particular, military conflicts, with their political implications and charged nature due to casualties, may be described in distinct ways by different language editions. while community guidelines ensure some quality control and consistency across articles, there are challenges to measuring these differences, though. language editions may differ because of (1) linguistic differences in expression; we narrow our scope of analysis to national entities and their contexts, posing the following computationally-amenable question about the representation of such entities: ## related work there is prior work extracting relations between and events involving geopolitical entities from text (o' @xcite @xcite @xcite ; see @xcite for a recent collection of papers. we focus on managing and comparing descriptions of such relations across different language communities @xcite . (of course, multilingual parallel and comparable corpora have been a mainstay of machine translation since its beginnings.) ## data collection our corpus of battle descriptions is collected from multiple language editions of wikipedia. to identify potential candidate articles for download, we take the names of articles listed under the english language categories "battles of world war i" and "battles of world war ii" foot_0 and corresponding categories in other language editions (e.g., ) " ≈ en" columns: % of articles in that language without an english-language equivalent. battaglie_della_prima_guerra_mondiale) identified by interlanguage wikilinks for german, french, and italian. these languages were selected because they are the primary languages employing latin script used by combatant countries with the largest recorded casualties. 2 different language editions do encompass different sets of articles, with some articles available in only a subset of data. so even if the communities are comprised of the same individuals with the same aims in every language edition, the output is non-equivalent for all languages. in total, our dataset has 765 distinct wwi battles and 3430 distinct wwii battles. see after the names of battle articles in different languages are collected, they are disambiguated by linking them to a wikidata item identifier known as a qid, obtained by querying the wikidata api. qids link articles across different language editions, and we use the reduced set of qids to identify all language editions of each article. though there is still a bias for articles grouped under the "battles of world war i" and "battles of world war ii" categories, this additional step reduces the likelihood that we are collecting data only visible from english wikipedia. for example, the de version of wikipedia tends to have fewer articles, possibly because they conceptualize warfare differently (e.g., campaigns instead of actions). full-text content is then downloaded from wikipedia using the petscan interface 3 . the next section discusses how this data is further cleaned https://en.wikipedia.org/wiki/category: battles_of_world_war_ii 2 we repeat that the restriction to latin script is an attempt to minimize processing differences between languages. moving to a larger set of more diverse languages is a direction for future work. and partitioned. ## associated languages and entities initially, all battle articles listed under the battle categories in each of the four languages are collected. but because this work compares language editions, only the intersection of the four language editions is used. this results in 131 articles for world war i and 414 articles for world war ii. this subset is then processed as described below. ## tuple clusters while the entities alone indicate a preference for language editions to reference their associated countries more, the context in which they occur may aid our understanding of why these differences in distribution occur. we hypothesized that overlap among languages may be more likely between english and french accounts and german and italian accounts than any combination of the two. but overlap alone says little about why accounts may differ. we simplify article text to (subject, object, relation) tuples. solely as a means to validate the quality of representation, a domain-specific outcome inference task is used. the intuition is that a better representation should enable a linear classifier to learn a correlation between outcome and text, among other properties. ## conclusion in this work, we introduced a methodology for identifying information upon which language editions agree and disagree by applying open-domain information extraction and unsupervised learning to english translations of articles. our results indicate that (1) language editions tend to mention their associated country more than other language editions mention the same country and (2) the fr language limitations there are limitations to using machine translation for historical analysis. to avoid issues regarding nuance, articles are reduced to a set of simple (subject, relation, object) tuples. the vector representations used were also evaluated on downstream tasks before use in our second experiment. future work there are several possible directions for future work. regarding tasks, it may be of interest to nlp practitioners to understand the impact the information imbalances have on downstream tasks such as translation. for the language communities themselves, it may be useful to be aware of the gaps in the accounts they are writing. to make this more useful for them, an important step would be to expand to other languages; our analysis is limited to four languages. future work should include articles from languages correlated with combatants on the western and pacific front. ultimately, more conclusive results will require a better model of the community dynamics and citation practices of editors, especially over time as well as more qualitative analysis of the differences between language editions. we aim to continue this work with the hope it encourages interest and advances in the overlap of computational, historical, and cultural analysis.