تعلم تمثيل كامن جيد ضروري لنقل نمط النص، والذي يولد جملة جديدة عن طريق تغيير سمات جملة معينة مع الحفاظ على محتواها.تعتمد معظم الأعمال السابقة تمثيل تمثيل كامن Disentangled تعلم تحقيق نقل النمط.نقترح خوارزمية نقل نمط النص الجديد مع تمثيل كامن متشابكا، وإدخال مصنف نمط يمكن أن ينظم الهيكل الكامن ونقل النقل.علاوة على ذلك، تنطبق خوارزمية لنقل النمط على كل من سمة واحدة ونقل السمة المتعددة.تظهر النتائج التجريبية الواسعة أن طريقتنا تتفوق بشكل عام على النهج الحديثة.
Learning a good latent representation is essential for text style transfer, which generates a new sentence by changing the attributes of a given sentence while preserving its content. Most previous works adopt disentangled latent representation learning to realize style transfer. We propose a novel text style transfer algorithm with entangled latent representation, and introduce a style classifier that can regulate the latent structure and transfer style. Moreover, our algorithm for style transfer applies to both single-attribute and multi-attribute transfer. Extensive experimental results show that our method generally outperforms state-of-the-art approaches.
References used
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