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Register a new userInjecting Entity Types into Entity-Guided Text Generation
أنواع حقن الكيان في جيل النص الموجه في الكيان
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أدت النجاحات الأخيرة في النمذجة التوليدية العميقة إلى تقدم كبير في توليد اللغة الطبيعية (NLG).أظهرت دمج الكيانات في نماذج الجيل العصبي تحسينات كبيرة من خلال المساعدة في استنتاج الموضوع الموجز وإنشاء محتوى متماسك.لتعزيز دور الكيان في NLG، في هذه الورقة، نهدف إلى نموذج نوع الكيان في مرحلة فك التشفير لتوليد كلمات سياقية بدقة.نقوم بتطوير نموذج NLG الجديد لإنتاج تسلسل مستهدف بناء على قائمة معينة من الكيانات.يحتوي نموذجنا على وحدة فك ترميز متعددة الخطوات التي تحفز أنواع الكيان في عملية تذكر الجيل.تجارب عملاء أخبار عامين تظهر حقن النوع ينفذ أفضل من نوع خطوط خطوط أسلاف التضمين.
Recent successes in deep generative modeling have led to significant advances in natural language generation (NLG). Incorporating entities into neural generation models has demonstrated great improvements by assisting to infer the summary topic and to generate coherent content. To enhance the role of entity in NLG, in this paper, we aim to model the entity type in the decoding phase to generate contextual words accurately. We develop a novel NLG model to produce a target sequence based on a given list of entities. Our model has a multi-step decoder that injects the entity types into the process of entity mention generation. Experiments on two public news datasets demonstrate type injection performs better than existing type embedding concatenation baselines.
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