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Register a new userCollaborative Data Relabeling for Robust and Diverse Voice Apps Recommendation in Intelligent Personal Assistants
البيانات التعاونية للتصليح لتوصية تطبيقات صوت قوية ومتنوعة في المساعدين الشخصيين الذكي
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مساعد شخصي ذكي (IPAS) مثل Amazon Alexa و Assistant Google وسيري سيري يمدد قدراتهم المدمجة من خلال دعم التطبيقات الصوتية التي طورها مطورو الطرف الثالث. في بعض الأحيان يكون المساعد الذكي غير قادر على الاستجابة بنجاح لأوامر صوت المستخدم (ويعرف أيضا باسم الكلام). هناك العديد من الأسباب بما في ذلك خطأ التعرف على الكلام التلقائي (ASR)، وفهم اللغة الطبيعية (NLU)، وتوجيه الكلام إلى تطبيق صوت غير ذي صحي أو ببساطة أن المستخدم يطلب إمكانية غير مدعومة بعد. يؤدي الفشل في التعامل مع أمر صوتي إلى إحباط العملاء. في هذه الورقة، نقدم نظام توصية مهارة الاحتياطية لاقتراح تطبيق صوتي إلى عميل أمر صوتي غير معالج. واحدة من التحديات البارزة في تطوير نظام موصوف المهارات ل IPAS هو ملاحظة جزئية. لحل مشكلة الملاحظة الجزئية، نقترح طريقة إعادة انتكاسات البيانات التعاونية (CDR). بالإضافة إلى ذلك، يحسن CDR أيضا تنوع المهارات الموصى بها. نقيم الطريقة المقترحة على حد سواء دون اتصال وعلى الإنترنت. تظهر نتائج التقييم غير المتصلة بالإنترنت أن النظام المقترح يتفوق على الأساس. تظهر نتائج اختبار Online A / B زيادة كبيرة من مقاييس تجربة العملاء.
Intelligent personal assistants (IPAs) such as Amazon Alexa, Google Assistant and Apple Siri extend their built-in capabilities by supporting voice apps developed by third-party developers. Sometimes the smart assistant is not able to successfully respond to user voice commands (aka utterances). There are many reasons including automatic speech recognition (ASR) error, natural language understanding (NLU) error, routing utterances to an irrelevant voice app or simply that the user is asking for a capability that is not supported yet. The failure to handle a voice command leads to customer frustration. In this paper, we introduce a fallback skill recommendation system to suggest a voice app to a customer for an unhandled voice command. One of the prominent challenges of developing a skill recommender system for IPAs is partial observation. To solve the partial observation problem, we propose collaborative data relabeling (CDR) method. In addition, CDR also improves the diversity of the recommended skills. We evaluate the proposed method both offline and online. The offline evaluation results show that the proposed system outperforms the baselines. The online A/B testing results show significant gain of customer experience metrics.
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