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توليد تسخير الهيكل المعزز
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تدرس هذه الورقة مهمة جيل تسييس الهدسة (KG) للسيناريوهات التي يلعب فيها الهيكل دورا مهما. على سبيل المثال، يتكون المنشور العلمي من عنوان قصير وجسم طويل، حيث يمكن استخدام العنوان لإلغاء التأكيد على التفاصيل غير المهمة في الجسم. وبالمثل، لوظائف وسائل التواصل الاجتماعي القصيرة (، تغريدات)، يمكن زيادة السياق النادر من الألقاب، على الرغم من أن غالبا ما تكون مفقودة. مساهمتنا هي توليد / زيادة الهيكل ثم حقن هذه المعلومات في الترميز، باستخدام أجهزة الرماية الحالية للمستندات الأخرى، تكمل عناوين مفقودة / غير كاملة. نقترحون نهج ترميز وثيقة المعزز في الهيكل الجديد تتكون من المراحل التالية: المرحلة الأولى، وهي توليد الهيكل، تمتد المستند المحدد بمخططات الرماية ذات الصلة ولكن غائبة، وتعزيز السياق المفقود. المرحلة الثانية، وهيكل الترميز، تقوم ببناء رسم بياني للمخططات الرائقية والوثيقة المعينة للحصول على تمثيل الهيكل المدرك للنص المعزز. تحقق نتائج التجريبية الخاصة بنا أن تكبير هيكلنا المقترح والترميز / فك التشفير المعزز يمكن أن يحسن كجم لكل من السيناريوهات، مما يتفوق على أحدث من الفن.
This paper studies the keyphrase generation (KG) task for scenarios where structure plays an important role. For example, a scientific publication consists of a short title and a long body, where the title can be used for de-emphasizing unimportant details in the body. Similarly, for short social media posts (, tweets), scarce context can be augmented from titles, though often missing. Our contribution is generating/augmenting structure then injecting these information in the encoding, using existing keyphrases of other documents, complementing missing/incomplete titles. We propose novel structure-augmented document encoding approaches that consist of the following two phases: The first phase, generating structure, extends the given document with related but absent keyphrases, augmenting missing context. The second phase, encoding structure, builds a graph of keyphrases and the given document to obtain the structure-aware representation of the augmented text. Our empirical results validate that our proposed structure augmentation and augmentation-aware encoding/decoding can improve KG for both scenarios, outperforming the state-of-the-art.
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