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Register a new userMulti-Style Transfer with Discriminative Feedback on Disjoint Corpus
تحويل النمط متعدد مع ردود فعل تمييزية على Confoint Corpus
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تم استكشاف نقل النمط على نطاق واسع في توليد اللغة الطبيعية مع استخراج Corpus غير الموازي بشكل مباشر أو غير مباشر فكرة من النمط من مصدر المجال المصدر والمستهدف. يعد القصور المشترك للنهج القائمة شرط التعليقات التوضيحية المشتركة في جميع الأبعاد الأسلوبية قيد النظر. يوفر توفر مجموعة البيانات هذه عبر مزيج من الأساليب من امتداد هذه الإعدادات إلى أبعاد أسلوب متعددة. في حين أن النماذج المتتالية الأبعاد المتتالية عبر أنماط متعددة هي احتمال، فإنه يعاني من خسارة المحتوى، خاصة عندما لا تكون أبعاد النمط مستقلة تماما عن بعضها البعض. في عملنا، نترخى هذا الشرط للبيانات المشروح بشكل مشترك عبر أنماط متعددة باستخدام البيانات المكتسبة بشكل مستقل عبر أبعاد نمط مختلفة دون أي شروح إضافية. نقوم بتهيئة إعداد برنامج فك تشفير التشفير مع طراز اللغة المستندة إلى المحول المدرب مسبقا على كائن عام وتعزيز قدرته على إعادة كتابة الأبعاد ذات الأرقام المستهدفة المتعددة عن طريق توظيف نماذج لغة واضحة على الطراز كتمييز. من خلال التقييم الكمي والنوعي، نعرض قدرة نموذجنا على التحكم في الأساليب عبر أبعاد أسلوب متعددة مع الحفاظ على محتوى نص الإدخال. قارنناها مع خطوط الأساس التي تنطوي على نماذج نقل أسلوب UNI-VIDELAL لحكام أحادي الأبعاد.
Style transfer has been widely explored in natural language generation with non-parallel corpus by directly or indirectly extracting a notion of style from source and target domain corpus. A common shortcoming of existing approaches is the prerequisite of joint annotations across all the stylistic dimensions under consideration. Availability of such dataset across a combination of styles limits the extension of these setups to multiple style dimensions. While cascading single-dimensional models across multiple styles is a possibility, it suffers from content loss, especially when the style dimensions are not completely independent of each other. In our work, we relax this requirement of jointly annotated data across multiple styles by using independently acquired data across different style dimensions without any additional annotations. We initialize an encoder-decoder setup with transformer-based language model pre-trained on a generic corpus and enhance its re-writing capability to multiple target style dimensions by employing multiple style-aware language models as discriminators. Through quantitative and qualitative evaluation, we show the ability of our model to control styles across multiple style dimensions while preserving content of the input text. We compare it against baselines involving cascaded state-of-the-art uni-dimensional style transfer models.
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