قد يتم تحديد الجنس النحاسي من قبل دلالات أو إجمالية أو علم الصوتيات أو يمكن أن يكون تعسفيا.تحديد الأنماط في العوامل التي تحكم نون الجنسين يمكن أن تكون مفيدة لمتعلمي اللغة، وفهم المصادر اللغوية الفطرية للتحيز بين الجنسين.قد يتم استبدال النهج اليدوية القائمة على القواعد اليدوية من خلال النهج الحسابية الأكثر دقة وقابلة للتطوير ولكن أصعب من أجل تفسيرها للتنبؤ بنوع الجنس من المعلومات النموذجية.في هذا العمل، نقترح نماذج تصنيف الجنسية القابلة للتفسير للفرنسية، والتي تحصل على أفضل ما في العالمين.نقدم نهج عصبي عالية الدقة التي تعززها نهج قائم على بديل عالمي جديد لتوضيح التنبؤات.نقدم سمات مساعدة "لتوفير تعقيد تفسير الضبط.
Grammatical gender may be determined by semantics, orthography, phonology, or could even be arbitrary. Identifying patterns in the factors that govern noun genders can be useful for language learners, and for understanding innate linguistic sources of gender bias. Traditional manual rule-based approaches may be substituted by more accurate and scalable but harder-to-interpret computational approaches for predicting gender from typological information. In this work, we propose interpretable gender classification models for French, which obtain the best of both worlds. We present high accuracy neural approaches which are augmented by a novel global surrogate based approach for explaining predictions. We introduce auxiliary attributes' to provide tunable explanation complexity.
References used
https://aclanthology.org/
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