Grammatical gender problem in Turkish-English translations and the effects of stereotypes on translation

English has become a widely used language in many sectors such as politics, economy, service, and health. Turkey is a country where these fields are highly demanded and it enabled the translation industry to develop both directly and indirectly. Translators, customers, and field researchers become more aware of translation errors and problems. As a result of these problems and errors, "equivalence" cannot be achieved at the desired level in translations done. The target text is expected to be equivalent to the source text and as the level of equivalence increases, the quality, reliability, and accuracy of the translation increases. Languages carry many clues about society and culture. The issue of gender is one of the areas where these clues can be caught because there are many differences in gender representation within languages. While Turkish does not have grammatical gender markings, the fact that English has these markings often brings up equivalence problems in translation. The research examines the translation problems experienced in cases where the third-person singular "o" (he/she/it) equivalence cannot be achieved in the translations from Turkish to English. It is aimed to explain the grammatical resolution strategies of these problems and the gender connotations of profession names in Turkish. Translations of the sentences containing doctor, teacher, engineer, lawyer, and, secretary were obtained from web-based machine translation software and active students of the preparatory, 1st, 2nd, 3rd, and 4th grades of the English Translation and Interpreting departments of the universities. The use of 3rd person singular pronouns in the translations was examined. To determine the gender connotations of profession names in Turkish, the data was taken from the Turkish National Corpus and the results were compared with both machine translations and student translations. It was observed that when equivalence could not be achieved in the 3rd person singular pronouns, both semantic and structural translation problems emerged and some of the students benefited from some strategies to cope with these problems. As a result of the data examined through the corpus, the gender connotations of the determined professions were explained. It was seen that the associations determined in natural language data had almost the same patterns in both human and machine translations.