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Register a new userGenerating Gender Augmented Data for NLP
توليد البيانات المعززة بين الجنسين ل NLP
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التحيز بين الجنسين هو حدوث متكرر في التطبيقات القائمة على البرامج الوطنية للتنمية الوطنية، وخاصة وضوحا باللغات التي يتم تنشيطها بين الجنسين. يمكن أن تظهر التحيز من خلال جمعيات بعض الصفات وتحريك الأسماء مع الجنس الطبيعي للإرازات، ولكن أيضا بسبب الترددات الجنسانية النحوية غير الموازنة من الكلمات المصدرة. يصبح هذا النوع من التحيز أكثر وضوحا في توليد ينصوصات التحدث حيث لا يتم تحديد النوع الاجتماعي داخل الجملة، لأن معظم تطبيقات NLP الحالية لا تزال تعمل على سياق مستوى الجملة. كخطوة نحو NLP أكثر شمولية، تقترح هذه الورقة نهجا تلقائيا وعموما إعادة كتابة جمل محادثة قصيرة. يمكن تطبيق طريقة إعادة كتابة على الجمل التي، دون سياق خارجي، لديها بدائل مكافئة متعددة من حيث الجنس. يمكن تطبيق الطريقة على إنشاء مخرجات متوازنة بين الجنسين وكذلك لإنشاء بيانات تدريب متوازنة بين الجنسين. يعتمد النهج المقترح على نظام ترجمة آلية عصبية مدربة على ترجمة "من نوع جنسي إلى آخر. إظهار كل من التحليل الأوتوماتيكي واليدوي للنهج النتائج الواعدة فيما يتعلق بالجيل التلقائي من البدائل الجنسانية لجمل المحادثة باللغة الإسبانية.
Gender bias is a frequent occurrence in NLP-based applications, especially pronounced in gender-inflected languages. Bias can appear through associations of certain adjectives and animate nouns with the natural gender of referents, but also due to unbalanced grammatical gender frequencies of inflected words. This type of bias becomes more evident in generating conversational utterances where gender is not specified within the sentence, because most current NLP applications still work on a sentence-level context. As a step towards more inclusive NLP, this paper proposes an automatic and generalisable re-writing approach for short conversational sentences. The rewriting method can be applied to sentences that, without extra-sentential context, have multiple equivalent alternatives in terms of gender. The method can be applied both for creating gender balanced outputs as well as for creating gender balanced training data. The proposed approach is based on a neural machine translation system trained to translate' from one gender alternative to another. Both the automatic and manual analysis of the approach show promising results with respect to the automatic generation of gender alternatives for conversational sentences in Spanish.
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