Gender and bias in A mazon review translations: by humans, MT systems and C hat GPT (2024)

Abstract: This paper presents an analysis of first-person gender in five different translation variants of Amazon product reviews:those produced by professional translators, by translation students, with different machine translation (MT) systems andwith ChatGPT. The analysis revealed that the majority of the reviews were translated into the masculine first-person gender, both by humans as well as by machines. Further inspection revealed that the choice of the gender in a translation is not related to the actual gender of the translator. Finally, the analysis of different products showed that there are certain bias tendencies, because the distribution of genders notably differ for different products.

## introduction in this paper, we focus on the distribution of gendered words in human and machine translations of product reviews from english into croatian and russian. in contrast to english, both croatian and russian have gender marking not only on pronouns, but also on nouns, adjectives, verbs, determiners and numbers. the gender implicit in the english source needs to be specified in the target. this may result in translation errors, mismatches and inconsistencies, as well as gender bias in train and test data. in reviews, the texts are written in the first person form as illustrated in example (1). while translating from english into croatian or russian, the gender of the adjectives and verb past and passive participles should be specified: обожал (masculine) vs. обожала (feminine). the decision for either feminine or masculine form is required not only in case of machine translation. human translators need to specify this form, too. if no information on the text author is available and no specific instructions are given for translators, this may result in inconsistencies and individual decisions by human translators. therefore, we decide to look into this variation analysing and comparing translations produced by two different groups of translators (professional and student) as well as with two machine translation systems and chatgpt large language model in the two language pairs at hand. our work is similar to the studies of gender bias in machine translation (mt). however, our primary focus is not on reducing the gender bias, but rather on regularities in human and machine translation data that may follow in the emerging gender bias in the data. gender bias (preference or toward one gender over the other) exists in training data, pre-trained models such as word embeddings and also algorithms themselves @xcite @xcite , so that a machine translation system containing bias can produce gender biased predictions. although this issue belong to active research topics, detection and evaluation of gender bias in machine translation systems have not been thoroughly investigated yet. in our analysis, we focus on the following research questions: rq1: what is the distribution of first person gender in different translations? rq2: is choice of the gender in human translations related to the gender of the translator? rq3: is choice of the gender related to the topic/product? the remainder of the paper is organised as follows: section 2 provides and overview of related studies. the data is described in section 3. the analyses and the results are presented in sections 4 and 5, and conclusions in section 6. ## related work our work is similar to the studies of gender bias in natural language processing and specifically in machine translation. however, our descriptive aims differ from those existing in most studies. some studies do describe bias in the data. for instance, @xcite addressed gender bias in word embeddings and @xcite provides an overview of existing biases. some works focus on the creation of challenge or test suites. @xcite presented a challenge set and evaluation protocol for the analysis of gender bias in mt. their automatic gender bias evaluation method was developed for eight target languages (including russian) with grammatical gender. they tested six mt systems themselves, including also google. @xcite presented an english-italian challenge set focusing on the resolution of natural gender phenomena by providing word-level gender tags on the english source side and multiple gender alternative translations, where needed, on the italian target side. the data analysed in our study can potentially serve as a test suite as well. in our work, we also address bias dependence on topic or product. similarly, bias variation was addressed in @xcite who found that on the one hand, data sets for specific tasks (e.g. cooking) contain significant gender bias and, on the other hand, models trained on these datasets further amplify existing bias. some works showed that bias can be measured, see e.g. @xcite who proposed a measure called 'translation gender bias index' (tgbi). we analyse both human and machine-translated texts. the latter were analysed in several other works. for instance, @xcite explored the potential of gender-inflection controlled translation in case the gender is identifiable either from a human reference or when it can be automatically gender-tagged. the authors found out that simple existing approaches could overgeneralize a gender-feature to multiple entities in a sentence, and suggested effective alternatives in the form of tagged co-reference adaptation data. they also proposed an extension to assess translations of gender-neutral entities from english given a corresponding linguistic convention in the target language. in another study, the authors analyse and evaluate gender bias comparing bias measurements across multiple metrics for pre-trained embeddings and the ones learned by their own machine translation model @xcite . a summary of various analyses of gender bias in machine translation was presented by @xcite . the authors also discussed the mitigating strategies proposed in various studies. @xcite presented a taxonomy of phenomena which caused bias in machine translation. interestingly, it included not only gender bias on people being male and female, but also number and formality bias (singular you vs. plural you as well as informal you vs. formal you). in our study, we focus not only on the machine translations but also on the human ones and compare them across each other. we also distinguish two groups of translators according to their experience: professionals and students. in this way, we also consider the bias introduced by the human translators, which has not been thoroughly analysed so far. human bias has been addressed in a few studies only. for instance, @xcite investigated the generation and consequent receptivity of manual annotators to bias of varying degrees. the authors created the first dataset of gpt-generated english text with normative ratings of gender bias. the variation of themes of gender biases in the observed ranking was then systematically analysed. the authors showed that identity-attack was most closely related to gender bias. they also showed the performance of existing automated models trained on related concepts on their dataset. we believe that our work has an added value to the studies existing in the area of machine transla-tion and natural language processing, as it adds to the awareness @xcite of the bias existing in the translation data, both in human and machine translations. ## data for our analysis, we use the publicly available corpus dihutra foot_0 @xcite . the corpus contains 196 english amazon product reviews (14 reviews in each of 14 different product categories) and their human and machine translations into three languages, croatian, russian and finnish. since the finnish language does not have grammatical gender in any word category, not even in personal pronouns, only croatian and russian were included in our analysis. the number of running words and vocabulary size for the source text and for each of the translations can be seen in in most of the reviews, the gender of the writer is not known, and not specified by any information in the english source. in two reviews only, the text indicates that the writer was a female. the human translations were produced by two groups of translators: several professional translators and several students. the translators were only instructed to keep the given segmentation and not to use any mt system. they did not receive any guidelines about how to treat the gender in the target language. therefore, the corpus is appropriate to explore the subjectivity. the machine translations in the corpus were generated by different mt systems. croatian mt outputs are the two best ranked outputs by human evaluation from the wmt 2022 shared task foot_1 @xcite . russian mt outputs were generated using google translate foot_2 and deepl translator foot_3 . chatgpt foot_4 translations for all target languages were generated using the publicly available gpt 3.5 version. since human translators were given only simple instructions, a similar approach was used for chatgpt as well, namely a simple prompt "translate into croatian/russian". ## analysis of first-person gender as mentioned in section 3, the gender of the writer is not known, and with the exception of two reviews, not specified by any information in the english source. therefore, the choice of the first person gender in the translation is totally free. the analysis of first-person gender was carried out manually, finding that the majority of the firstperson gendered words are verb past participles, followed by adjectives and verb passive participles. this analysis revealed that some student translations and many chatgpt translations contain the inclusive gender forms. these words were not properly recognised by the part-of-speech tagger and were tagged as masculine nouns. for each review, a gender label was assigned according to the gendered words it contained. if all first-person gendered words within a review have the same gender (feminine, masculine or inclusive), the review was assigned this gender label. if there was a mixture of first-person genders, the review got the label "mixed". an example of gender labels for croatian and russian translations foot_5 is shown in it should be noted that there are still no nonbinary forms in the analysed target languages. neuter gender is never used for people, only for objects, and would sound awkward, and even possibly offensive. also, while in some texts it is possible to avoid the gender and generate a "neutral" translation, it is very difficult to avoid all adjectives and past participles. the only way for a proper inclusion is to use the "inclusive" form, comprising both gender variants in a word. ## conclusions this work presents results of analysis of firstperson gender in russian and croatian translations of english user reviews. we addressed three research questions concerning the distribution of the first person gender, the relation between the choice of the gender for translation and the real gender of the translator, as well as a tendency towards a product or product group bias. we group the findings according to the three research questions addressed: rq1: what is the distribution of first person gender in different translations? we could observe that in all translations, the predominant gender is masculine. inter-estingly, the difference is much stronger in machine-translated texts. this indicates the intensification of the gender bias existing in human translations. rq2: is choice of the gender in human translations related to the gender of the translator? our data shows that it is not the case. all translators in our dataset at hand, regardless of their gender, translated more reviews into the masculine form. it is interesting to note that we also observed the cases of a male translator using feminine forms. rq3: is choice of the gender related to the topic/product? although the data set is too small to draw hard conclusions, we noticed a clear tendency, especially in human translations. similar tendencies are observed in machinegenerated output, although the overall trend is notably less feminine translations in each of the product categories. the reported findings also open several directions for future work. apart from including more target languages from different families, as well as more domains and topics, more language models should be included, also the outputs using different prompts such as giving particular instructions regarding gender specification. furthermore, a test suite specifically designed for first-gender analysis should be used in future experiments.