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Register a new userContrastive Representation Learning for Exemplar-Guided Paraphrase Generation
التعلم التمثيل من النقيض من أجل توليد إعادة صياغة النص
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تهدف توليد الصياغة الموجهة إلى Exemplar (EGPG) إلى توليد جملة مستهدفة تتوافق مع أسلوب Exemplar المحدد أثناء توسيع نطاق معلومات المحتوى من الجملة المصدر. في هذه الورقة، نقترح طريقة جديدة بهدف تعلم تمثيل أفضل للنمط والمحتوى. تحفز هذه الطريقة بشكل أساسي النجاح الأخير للتعلم المقاوم للتناقض التي أثبتت قوتها في مهام استخراج ميزة غير مدفوعة. تتمثل الفكرة في تصميم خسائرتين متباينتين فيما يتعلق بالمحتوى والأسلوب من خلال النظر في خصائص المشكلة أثناء التدريب. إحدى الممتلكات هي أن الجملة المستهدفة تشترك في نفس المحتوى مع جملة المصدر، والخصائص الثانية هي أن الجملة المستهدفة تشارك نفس النمط مع Exemplar. يتم دمج هذين الخسائرتين للتناقض في نموذج فك التشفير العام. تثبت التجارب على مجموعة بيانات اثنين، وهي QQP-Pos و Paranmt، فعالية خسائرنا القاطعة المقترحة.
Exemplar-Guided Paraphrase Generation (EGPG) aims to generate a target sentence which conforms to the style of the given exemplar while encapsulating the content information of the source sentence. In this paper, we propose a new method with the goal of learning a better representation of the style and the content. This method is mainly motivated by the recent success of contrastive learning which has demonstrated its power in unsupervised feature extraction tasks. The idea is to design two contrastive losses with respect to the content and the style by considering two problem characteristics during training. One characteristic is that the target sentence shares the same content with the source sentence, and the second characteristic is that the target sentence shares the same style with the exemplar. These two contrastive losses are incorporated into the general encoder-decoder paradigm. Experiments on two datasets, namely QQP-Pos and ParaNMT, demonstrate the effectiveness of our proposed constrastive losses.
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