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Register a new userEvent Representation with Sequential, Semi-Supervised Discrete Variables
تمثيل الأحداث بمتغيرات منفصلة متسلسلة وشبه إشراف
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في سياق نمذجة الأحداث والتفاهم، نقترح طريقة جديدة للنمذجة التسلسل العصبية التي تأخذ تسلسلات ملحوظة جزئيا من المعرفة المنفصلة والخارجية في الاعتبار.نقوم بإنشاء AutoNcoder Neal STUITENCODER المتسلسل، والذي يستخدم إعادة تجديد Gumbel-Softmax ضمن تشفير محددة بعناية، للسماح بعملي ناجح خلال التدريب.تتمثل الفكرة الأساسية في السماح بالمعرفة المنفصلة الخارجية شبه الإشراف للتوجيه، ولكن لا تقيد، المعلمات الكامنة المتنوعة أثناء التدريب.تشير تجاربنا إلى أن نهجنا لا يتفوق فقط على خطوط أساسية متعددة وحديث الحديث في تحريض النصي السردي، ولكن أيضا التقارب بسرعة أكبر.
Within the context of event modeling and understanding, we propose a new method for neural sequence modeling that takes partially-observed sequences of discrete, external knowledge into account. We construct a sequential neural variational autoencoder, which uses Gumbel-Softmax reparametrization within a carefully defined encoder, to allow for successful backpropagation during training. The core idea is to allow semi-supervised external discrete knowledge to guide, but not restrict, the variational latent parameters during training. Our experiments indicate that our approach not only outperforms multiple baselines and the state-of-the-art in narrative script induction, but also converges more quickly.
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