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Register a new userBootstrapping a Music Voice Assistant with Weak Supervision
bootstrapping مساعد صوت الموسيقى مع الإشراف ضعيف
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أحد كتل المبنى الأولى لإنشاء مساعد صوت يتعلق بمهمة وضع علامة الكيانات أو السمات في استعلامات المستخدم. يمكن أن يكون هذا تحديا بشكل خاص عندما تكون الكيانات في العاشر من الملايين، كما هو الحال على سبيل المثال كتالوجات الموسيقى. تتطلب نماذج وضع العلامات التدريبية التدريبية على نطاق صناعي كميات كبيرة من استفسارات المستخدم المسمى بدقة، والتي غالبا ما تكون صعبة ومكلفة لجمعها. من ناحية أخرى، جمع المساعدون الصوتيين عادة الكثير من الاستفسارات غير المستمرة التي لا تزال غير منفصلة في كثير من الأحيان. تقدم هذه الورقة منهجية خاضعة للإشراف ضعيفا لتسمية كميات كبيرة من سجلات الاستعلام الصوتية، معززة مع خطوة تصفية يدوية. تظهر التقييمات التجريبية لدينا أن نماذج علامات الشريعة المدربة على البيانات المتفوقة على البيانات التي تجريب ضعيف تدربت على البيانات المشروحة باليد أو الاصطناعية، بتكلفة أقل. علاوة على ذلك، يؤدي التصفية اليدوية للبيانات الخاضعة للإشراف إلى انخفاض كبير في معدل خطأ العقوبة، مع السماح لنا بتقليل جهود الرعاية البشرية بشكل كبير من أسابيع إلى ساعات، فيما يتعلق بالتعليق اليدوي للاستفسارات. يتم تطبيق الطريقة بنجاح Bootstrap نظام علامات Slot لخدمة تدفق الموسيقى الرئيسية التي تخدم حاليا العديد من عشرات الآلاف من الاستفسارات الصوتية اليومية.
One of the first building blocks to create a voice assistant relates to the task of tagging entities or attributes in user queries. This can be particularly challenging when entities are in the tenth of millions, as is the case of e.g. music catalogs. Training slot tagging models at an industrial scale requires large quantities of accurately labeled user queries, which are often hard and costly to gather. On the other hand, voice assistants typically collect plenty of unlabeled queries that often remain unexploited. This paper presents a weakly-supervised methodology to label large amounts of voice query logs, enhanced with a manual filtering step. Our experimental evaluations show that slot tagging models trained on weakly-supervised data outperform models trained on hand-annotated or synthetic data, at a lower cost. Further, manual filtering of weakly-supervised data leads to a very significant reduction in Sentence Error Rate, while allowing us to drastically reduce human curation efforts from weeks to hours, with respect to hand-annotation of queries. The method is applied to successfully bootstrap a slot tagging system for a major music streaming service that currently serves several tens of thousands of daily voice queries.
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State-of-the-art deep neural networks require large-scale labeled training data that is often expensive to obtain or not available for many tasks. Weak supervision in the form of domain-specific rules has been shown to be useful in such settings to a
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