نقترح نهجا متعدد المهام، وهو نهج احتمالي لتسهيل استخراج العلاقات بالإشراف المستمر عن طريق إحضار أوثق تمثيل الجمل التي تحتوي على نفس أزواج قاعدة المعرفة.لتحقيق ذلك، نحن نحيز المساحة الكامنة من الجمل عبر السيارات الآلية (VAE) التي يتم تدريبها بشكل مشترك مع مصنف العلاقة.يرشد القانون الكامن تمثيلات الزوج وتؤثر إعادة إعمار الجملة.تشير النتائج التجريبية إلى مجموعة البيانات التي تم إنشاؤها عبر الإشراف البعيد إلى أن التعلم متعدد المهام ينتج عن فوائد الأداء.يكشف الاستكشاف الإضافي لتوظيف برايورس قاعدة المعارف في TheVAE أن مساحة الجملة يمكن أن تتحول نحو قاعدة المعرفة، وتقديم الترجمة الترجمة الترجمة
We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Knowledge Base pairs. To achieve this, we bias the latent space of sentences via a Variational Autoencoder (VAE) that is trained jointly with a relation classifier. The latent code guides the pair representations and influences sentence reconstruction. Experimental results on two datasets created via distant supervision indicate that multi-task learning results in performance benefits. Additional exploration of employing Knowledge Base priors into theVAE reveals that the sentence space can be shifted towards that of the Knowledge Base, offering interpretability and further improving results.
References used
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