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Register a new userMatching-oriented Embedding Quantization For Ad-hoc Retrieval
قياس التضمين التضمين المطابقة لاسترجاع المخصص
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كمية المنتج (PQ) هي تقنية واستخدامها على نطاق واسع لاسترجاع الإعلانات المخصصة. تقترح الدراسات الحديثة PQ خاضعة للإشراف، حيث يمكن تدريب نماذج التضمين والتجميل بشكل مشترك مع التعلم الخاضع للإشراف. ومع ذلك، هناك نقص في الصياغة المناسبة لهدف التدريب المشترك؛ وبالتالي، فإن التحسينات حول الأساس غير المشرف السابق محدودة في الواقع. في هذا العمل، نقترح قياس كمية المنتج الموجهة نحو المطابقة (MOPQ)، حيث يتم صياغة فقدان MultioLli Outlastive MultioLli مهدفا. مع تقليل MCL، نحن قادرون على زيادة احتمال مطابقة الاستعلام ومفتاح الحقيقة الأرضية، مما يساهم في دقة الاسترجاع المثلى. بالنظر إلى أن الحساب الدقيق ل MCL مستعصرا بسبب طلب عينات متباينة واسعة، فإننا نقترح مزيد من أخذ العينات عبر الأجهزة المختلفة (DCS)، والذي يزيد بشكل كبير من العينات المقنعة لتقريب دقيق من MCL. نقوم بإجراء دراسات تجريبية واسعة النطاق على أربعة مجموعات بيانات حقيقية، والتي تحقق نتائجها من فعالية MOPQ. الرمز متاح في https://github.com/microsoft /mopq.
Product quantization (PQ) is a widely used technique for ad-hoc retrieval. Recent studies propose supervised PQ, where the embedding and quantization models can be jointly trained with supervised learning. However, there is a lack of appropriate formulation of the joint training objective; thus, the improvements over previous non-supervised baselines are limited in reality. In this work, we propose the Matching-oriented Product Quantization (MoPQ), where a novel objective Multinoulli Contrastive Loss (MCL) is formulated. With the minimization of MCL, we are able to maximize the matching probability of query and ground-truth key, which contributes to the optimal retrieval accuracy. Given that the exact computation of MCL is intractable due to the demand of vast contrastive samples, we further propose the Differentiable Cross-device Sampling (DCS), which significantly augments the contrastive samples for precise approximation of MCL. We conduct extensive experimental studies on four real-world datasets, whose results verify the effectiveness of MoPQ. The code is available at https://github.com/microsoft/MoPQ.
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