Assessing Gender Bias in Machine Translation

Machine translation systems, such as Google Translate and Bing Microsoft Translator, are widely utilized for translating text from one language to another. This study undertakes a comprehensive evaluation of Google Translate and Bing Microsoft Translator with a specific focus on natural gender translation. Through rigorous analysis, it assesses the occurrence of female, male, and neutral forms in the translations of personality adjectives, professions, and nouns. Emphasizing the English to Hindi language pair, the evaluation meticulously examines how effectively these translation systems handle gendered language nuances, particularly in professions where biases may exist. By providing statistical insights into the translation outputs, the study aims to shed light on potential biases and accuracy levels within these machine translation systems. Ultimately, this evaluation contributes to a deeper understanding of the capabilities and limitations of automated translation tools in accurately representing gender-specific language constructs, thereby facilitating more nuanced and sensitive cross-lingual communication.