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Register a new userA Hybrid Approach to Scalable and Robust Spoken Language Understanding in Enterprise Virtual Agents
نهج هجين في فهم اللغة المنطوقة القابلة للتطوير والتحدث في الوكلاء الافتراضيين للمؤسسات
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فهم اللغة المنطوقة (SLU) يستخرج المتوسط المقصود من كلام المستخدم وهو عنصر حرج في عوامل المحادثة الافتراضية.في الوكلاء الافتراضيين للمؤسسة (EVAS)، فهم اللغة تحديا كبيرا.أولا، المستخدمين متصلون نادرون غير مألوفين بتوقع تدفق محادثة مصممة مسبقا.ثانيا، يدفع المستخدمون للعملاء من المؤسسة الذين يطالبون بتجربة مستخدم موثوقة ومتسقة وفعالة عند حل مشكلاتها.في هذا العمل، نصف إطارا عاما وقوي لاستخراج النوايا والكيان باستخدام هجينة من النهج الإحصائية القائمة على القواعد.يشمل إطارنا نمذجة الثقة التي تتضمن معلومات من جميع المكونات في خط أنابيب Slu، إضافة نقدية للإستقاط إلى الدقة بالتأكيد.يركز تركيزنا على إنشاء وحدة دقيقة وقابلة للتطوير التي يمكن نشرها بسرعة للحصول على فئة كبيرة من تطبيقات إيفا مع القليل من الحاجة إلى التدخل البشري.
Spoken language understanding (SLU) extracts the intended mean- ing from a user utterance and is a critical component of conversational virtual agents. In enterprise virtual agents (EVAs), language understanding is substantially challenging. First, the users are infrequent callers who are unfamiliar with the expectations of a pre-designed conversation flow. Second, the users are paying customers of an enterprise who demand a reliable, consistent and efficient user experience when resolving their issues. In this work, we describe a general and robust framework for intent and entity extraction utilizing a hybrid of statistical and rule-based approaches. Our framework includes confidence modeling that incorporates information from all components in the SLU pipeline, a critical addition for EVAs to en- sure accuracy. Our focus is on creating accurate and scalable SLU that can be deployed rapidly for a large class of EVA applications with little need for human intervention.
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