D i H u T ra: a Parallel Corpus to Analyse Differences between Human Translations (2022)

Abstract: This project aimed to design a corpus of parallel human translations (HTs) of the same source texts by professionals and students. The resulting corpus consists of English news and reviews source texts, their translations into Russian and Croatian, and translations of the reviews into Finnish. The corpus will be valuable for both studying variation in translation and evaluating machine translation (MT) systems.

## description many studies have demonstrated that translated texts have different textual features than texts originally written in the given language (originals). furthermore, some studies have shown evidence of variation between human translations generated by different translators (rubino et al., 2016; popović, 2020; kunilovskaya and lapshinova-koltunski, 2020). nevertheless, the number of such studies is still very small and limited to comparable corpora where different translators translated different source texts. therefore, exact comparisons between human translations are not possible. the dihutra project, formed by saarland university, adapt centre and university of eastern finland in 2021-2022 has aimed to design a parallel corpus to address these issues. each source text originally written in english has been translated into three target languages: croatian, russian and finnish, by two groups of translators: professionals and students. these parallel human translations c 2022 the authors. this article is licensed under a creative commons 3.0 licence, no derivative works, attribution, cc-by-nd. ## data sets the source texts consist of two sub-sets of publicly available data sets from two distinct domains: amazon product reviews foot_0 contain unique product reviews from amazon written in english with overall ratings from 1 to 5, 1 and 2 referring to negative, 3 to neutral and 4 and 5 to positive. we selected a balanced set of reviews from 14 categories (e.g., "sports and outdoors", "books", etc.) with an equal number of positive and negative reviews (14 from each of the 14 topics). in total, we included 196 reviews, containing 5.4 sentences and 93.2 words on average. news texts were imported from the wmt (2019 and 2020) shared task foot_1 news test corpus. the topics vary between politics, sports, crime, health, etc. the news are longer than reviews, with 9.9 sentences and 221.7 words on average. the wmt shared tasks also contain a set of human translations of the english source texts into several languages including russian, however, neither croatian nor finnish. we selected only texts which were originally written in english and had professional translations into russian. in total, we included 68 news articles from different sources. ## translation process each english review was translated into the three target languages, croatian, russian and finnish, by professionals and by students. for the news corpus, russian translations were already available from the wmt shared task and croatian translations were produced for the purpose of this work. finnish professional translations were not provided for the news articles. in addition to translations, information about age, gender, experience and the study program (for students) was collected. translators were asked to keep the sentence alignment (not to merge or to split sentences) and not to use mt. no further restrictions were given to translators. the total number of tokens in the resulting corpus amounts to 180,584. ## corpus statistics the first statistics on the shallow features in terms of running words and vocabulary in the sources and the three target languages (see the corpus is available via clarin @xcite . the project has also a github repository foot_3 which contains the data and some additional information. the details about the corpus can be found in (lapshinova-koltunski et al., 2022).