The predictability of literary translation (2022)

Abstract: Research has shown that the practice of translation exhibits predictable linguistic cues that make translated texts detectable from original-language texts (a phenomenon known as “translationese”). In this paper, we test the extent to which literary translations are subject to the same effects and whether they also exhibit meaningful differences at the level of content. Research into the function of translations within national literary markets using smaller case studies has suggested that translations play a cultural role that is distinct from that of original-language literature, i.e. their differences reside not only at the level of translationese but at the level of content. Using a dataset consisting of original-language fiction in English and translations into English from 120 languages (N=21,302), we find that one of the principal functions of literary translation is to convey predictable geographic identities to local readers that nevertheless extend well beyond the foreignness of persons and places.

## introduction translation plays an important role in the international circulation of stories and ideas. translations allow for the more widespread circulation of writing that would otherwise be hindered by global language differences. as such, translations can provide insights not only into the global commerce of ideas, but also the ways in which local regional cultures represent world cultures through their selection of works for translation. research in corpus linguistics has consistently shown that the practice of translation is subject to producing predictable linguistic cues that distinguish translated texts from original-language texts regardless of the source or target languages @xcite @xcite . from this perspective translation is understood as a particular "register" of language (called "translationese") governed by the cognitive demands of moving between languages @xcite @xcite . at the same time, the field of literary translation studies has developed frameworks for understanding the concrete translational practices that arise in different national and historical settings. relying mostly on smaller case studies, researchers have shown how particular cultural norms, political ideologies, and institutional contexts affect the nature and selection of literary translations within national literary markets @xcite @xcite . @xcite and @xcite have illustrated the asymmetry of target and source languages in international translation markets, i.e. the way translations are highly concentrated within a few core languages. @xcite and @xcite have also shown how translations are often dominated by already highly reprinted canonical literature, where literary translation assumes a function of cultural consecration. our aim in this paper is to test the extent to which literary translations exhibit predictable traits similar to translationese but that reside at a deeper level of thematic content. do translations function in a sense like a distinct literary genre, communicating a predictable set of themes that are otherwise less prevalent within original-language fiction? understanding this aspect of translations' coherence will help us better understand the cultural functions that translations potentially serve. our goal in doing so is to bring the affordances of nlp and machine learning into conversation with the work of cul-tural sociology and translation studies to further our understanding of the larger cultural function of translations in different literary contexts. ## data for this paper, we follow the lead of @xcite and create two equal-sized corpora of fictional texts, one consisting of works originally written in english and one of works translated into english from other languages. our data is drawn from the nov-eltm data-set of english-language fiction, which identifies 176,000 volumes of fiction located in the hathitrust digital library published since the eighteenth century @xcite . in order to identify a work as a translation, we use a set of regular expressions such as "translated from," "from the [language]," "tr. from," "rendered into english," etc. and match in volume metadata provided by hathi to identify an initial list of candidates. if an author is included in this initial list, we then include all titles by that author. in order to identify a volume as an originallanguage work, we use fuzzy matching against a large set of author names derived from wikipedia and the virtual international authority file (viaf), a database of author names from 69 library catalogues from around the world and their original language of publication. we limit publication date between 1950-2008 for two reasons. the period after wwii is often considered a unique period in literary history, and thus these boundaries allow researchers to study translations as part of "post-war" literary culture. additionally, we found that the diversity of source languages is almost exclusively european prior to this date, limiting the relevance of the data for studying questions concerning geographic space and language. finally, we also remove all volumes where underwood's predicted probability of being non-fiction was greater than 85%. given that the set of original language works was larger than the set of translations, we then downsample each year of our original publications to match the number of translations. in order to prepare texts for analysis, we concatenate the individual page files from each volume into a single document. we then represent each document as ten randomly selected 1,000-continuousword samples drawn from the middle 60% of the document to avoid paratextual content in the front and backmatter. in order to avoid instances of low ocr quality, foreign-language passages, and samples that might have non-standard characters, only samples that have 90% of words in an english dictionary comprised of english-language fiction were kept. if a work did not have ten samples that met this criteria it was removed. after final review and cleaning we ended up with samples from 10,657 originals and 10,645 translations published since 1950. our data contains 9,701 authors and translations from 120 unique languages. fig. 1 provides the distribution of volumes by decade, while fig. 2 provides the distribution of volumes by language region. as we can see the hathi trust collection is heavily biased towards translations from european languages. to our knowledge, no existing collection of historically-matched translated and target-language fictional texts approaches the size or linguistic diversity of our corpus, and we hope that it will serve as a resource for additional research. figure 2: distribution of volumes by language region to approximate translationese, we utilize a feature space composed of non-semantically valuable words, also known as "function words." as prior research has shown, numerous qualities of translationese -from the differential rate of pronouns (explicitation) to the recourse to shorter words (simplification) to the misalignment with expected word probabilities due to thinking in two languages simultaneously (interference) -are encoded in function words @xcite . for our implementation, we use the 153 english stop-words included in the nltk library as our first feature space. to capture content-level differences between translations and original-language works, we focus on constructing a feature space that is composed of semantically rich words that are not overtly culturally specific. as @xcite have argued, translations are highly recognizable when compared to originals because they de facto contain foreign places and proper names. the classification task is thus often seen in this regard as a trival undertaking. our goal is to assess the predictability of translation while masking these overt regional references. to do so, we first construct a list of the most frequent (non-lemmatized) words that appear in our corpus not including stopwords. we then further manually remove proper names, locations, foreign words, and any obvious references to specific cultures or regions (such as "madame" or "rupees"). after manual cleaning, we limit the number of words to a total of 2,000. as an additional step towards masking the effects that individual and culturally-specific keywords may have, we further refine our feature space by adopting the procedure known as "authorless topic modeling" @xcite . authorless topic modeling is appropriate for our purposes because it corrects for the tendency of lda to generate overly source-specific results, especially topics that reflect key terms from a specific author or in this case language. by probabilistically subsampling words and eliminating those that are highly correlated with corpus metadata, this method helps reduce the association between particular topics and source texts, thereby producing more generalizable topics across the whole corpus. after experimentation we settle on a 30 topic model as the optimal representation. we provide samples of our topics in because we are interested in assessing the extent to which content-level distinctions are potentially geographically dependent, we rerun the above two steps only on the translation data (i.e. we generate new lists of most frequent words, clean and re-apply authorless lda). we then partition our translation data according to two different scenarios. the first is based on assumptions in the field of world literary studies that models the literary sphere into a european "centre" and non-european "periphery" @xcite . the second subsets texts by each major geographic region as listed in overall, this results in a total of five prediction tasks, four binary and one multiclass (see ## results we present our results in ## discussion our paper provides the first ever attempt to use natural language processing to assess 1) whether literary translations exhibit categorically different behavior at the level of content when compared to original-language literature, and 2) whether these differences can be reliably mapped onto specific geographical regions while masking geographic information. we have found that while literary translations do indeed exhibit predictable qualities of translationese, they register even stronger stylistic differences at the level of content. most notably, this holds even when explicit references to cultural contexts have been removed. literary translation is distinctive as a class of writing because it talks about different kinds of experiences in different ways than original language literature. this insight should motivate a good deal of future research into further understanding the particular nature of these differences. while prior work has suggested that literary translation plays a largely hierarchizing function -i.e. reproduces cultural hierarchies by conditioning on already highly reproduced (canonical) works -we find that literary translations are also distinctive because they introduce alternative subject matter into a target language that is geographically predictable even without overt geographical and cultural identifiers. this suggests to us that one of translation's cultural functions is to encode geographic space, not simply through proper names or locations, but through a more extensive semantic field of references. translations, in other words, make foreign spaces predictable and familiar to readers. to further unpack the relationship between particular topics and translations from different regions, we use a z-score calculation to determine which topics were more distinctive for individual regions, as shown in topic 17 turns out to be most distinctive for south asia in particular, suggesting that works from this region that are translated often feature depictions of traditional village life. the east asian topics prove difficult to parse without additional investigation but suggest a focus on morality (topic 6) and merchantry (topic 16). the relatively high representation of topic 28 in middle eastern texts indicates a predictable emphasis on religious matters. and finally, the "african" topic 7 suggests a concern with war and politics, possibly reflecting the postcolonial concerns of post-wwii african fiction. topic 22 seems rather diffuse, but a glance at the texts in which it has strong representation reveal that it is associated with folk and fairy tales, which again suggests a stereotypical approach to translations from african languages. measuring the predictability of translation at the level content allows us to better understand the ways in which different regions and languages are represented in english. studying translation at this level of scale can offer insights into how different regions consume and portray the world beyond their borders. while our work offers an initial insight into the function of translations into english, future work will want to compare these results with other regional and linguistic contexts. how do different regions represent world cultures differently when compared to each other? our work offers a framework that can be applied to future parallel datasets to further understand the role that translation plays in shaping the global literary marketplace.