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NLP Approaches for Bidirectional Translation Between Genderless and Gender-Defined Languages

  • Unique Paper ID: 176894
  • Volume: 11
  • Issue: 11
  • PageNo: 6415-6419
  • Abstract:
  • Languages vary significantly in how they express gender, posing unique challenges for natural language processing (NLP) systems tasked with bidirectional translation between genderless and gender-defined languages. This paper explores computational approaches for achieving accurate and contextually appropriate translations across this linguistic divide. We investigate the implications of gender omission or specification in machine translation, focusing on preserving meaning, maintaining fairness, and avoiding gender bias. Our work evaluates transformer-based models, gender annotation techniques, and context-aware embeddings to improve performance in both directions—translating from genderless to gender-defined languages and vice versa. Experiments are conducted on datasets involving pairs such as English–Turkish, English–Hebrew, and English–Finnish, with attention to pronoun disambiguation, occupational nouns, and sociolinguistic context. Results show that incorporating explicit gender cues and fine-tuning with gender-annotated corpora significantly enhances translation quality and fairness. This study contributes to building more inclusive and linguistically sensitive NLP systems, and highlights the need for culturally informed datasets and evaluation metrics.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 11
  • PageNo: 6415-6419

NLP Approaches for Bidirectional Translation Between Genderless and Gender-Defined Languages

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Journal no 47859

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