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Register a new userGeneric resources are what you need: Style transfer tasks without task-specific parallel training data
الموارد العامة هي ما تحتاجه: مهام نقل النمط دون بيانات تدريب موازية محددة
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تهدف نقل النمط إلى إعادة كتابة نص مصدر بأسلوب مستهدف مختلف مع الحفاظ على محتواها. نقترح نهجا جديدا لهذه المهمة التي تنفد على الموارد العامة، ودون استخدام أي بيانات متوازية (الهدف - المستهدفة (المصدر) تفوقت على النهج الموجودة غير المنشورة على مهام نقل النمط الأكثر شعبية: نقل الشكليات ومبادلة القطبية. في الممارسة العملية، نعتمد إجراء متعدد الخطوات الذي يبني على نموذج تسلسل تسلسل مسبقا عام (BART). أولا، نقوم بتعزيز قدرة النموذج على إعادة الكتابة عن طريق مزيد من الردف ما قبل التدريب على كل من مجموعة موجودة من الصيارات العامة، وكذلك على أزواج الاصطناعية التي تم إنشاؤها باستخدام مورد مجمع للأغراض العامة. ثانيا، من خلال نهج الترجمة مرة أخرى تكرارية، نقوم بتدريب نماذجين، كل منها في اتجاه نقل، حتى يتمكنوا من توفير بعضهم البعض مع أزواج توليد مزخرف، ديناميكيا في عملية التدريب. أخيرا، ندعنا نطاطنا الناتج لدينا تولد أزواجا صناعية ثابتة لاستخدامها في نظام تدريبي مشترك. إلى جانب المنهجية والنتائج الحديثة، فإن المساهمة الأساسية لهذا العمل هي انعكاس على طبيعة المهامتين التي نتعامل معها، وكيف يتم تمييز اختلافاتهم عن طريق ردهم على نهجنا.
Style transfer aims to rewrite a source text in a different target style while preserving its content. We propose a novel approach to this task that leverages generic resources, and without using any task-specific parallel (source--target) data outperforms existing unsupervised approaches on the two most popular style transfer tasks: formality transfer and polarity swap. In practice, we adopt a multi-step procedure which builds on a generic pre-trained sequence-to-sequence model (BART). First, we strengthen the model's ability to rewrite by further pre-training BART on both an existing collection of generic paraphrases, as well as on synthetic pairs created using a general-purpose lexical resource. Second, through an iterative back-translation approach, we train two models, each in a transfer direction, so that they can provide each other with synthetically generated pairs, dynamically in the training process. Lastly, we let our best resulting model generate static synthetic pairs to be used in a supervised training regime. Besides methodology and state-of-the-art results, a core contribution of this work is a reflection on the nature of the two tasks we address, and how their differences are highlighted by their response to our approach.
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