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Register a new userLeveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking
الاستفادة من أوصاف الفتحة لحضور حوار النطاق الصفر
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تمكننا تتبع حالة الحوار عبر المجال الصفرية (DST) من التعامل مع المجالات غير المرئية دون حساب جمع البيانات داخل المجال.في هذه الورقة، نقترح وصفات فتحة معززة النهج الإداري المعزز ل DST الصفرية عبر DST.على وجه التحديد، يقوم نموذجنا أولا بتشميز سياق الحوار وفتحة مع ترميز من يقارب الذات المدرب مسبقا، ويولد قيمة فتحة بطريقة تراجع تلقائي.بالإضافة إلى ذلك، ندمج نوع الفتحات الوصف المستنيرة التي تلتقط المعلومات المشتركة من فتحات مختلفة لتسهيل نقل المعرفة عبر المجال.توضح النتائج التجريبية على MultiWoz أن طرازنا يحسن بشكل كبير من نتائج أحدث النتائج الموجودة في إعداد المجال المتقاطع Zero-Shot.
Zero-shot cross-domain dialogue state tracking (DST) enables us to handle unseen domains without the expense of collecting in-domain data. In this paper, we propose a slot descriptions enhanced generative approach for zero-shot cross-domain DST. Specifically, our model first encodes a dialogue context and a slot with a pre-trained self-attentive encoder, and generates slot value in auto-regressive manner. In addition, we incorporate Slot Type Informed Descriptions that capture the shared information of different slots to facilitates the cross-domain knowledge transfer. Experimental results on MultiWOZ shows that our model significantly improve existing state-of-the-art results in zero-shot cross-domain setting.
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Recent task-oriented dialogue systems learn a model from annotated dialogues, and such dialogues are in turn collected and annotated so that they are consistent with certain domain knowledge. However, in real scenarios, domain knowledge is subject to
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