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Register a new userA Label-Aware BERT Attention Network for Zero-Shot Multi-Intent Detection in Spoken Language Understanding
شبكة Bertway Network Network للكشف عن صفر طلقة متعددة النوايا في فهم اللغة المنطوقة
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مع النجاح المبكر لمساعدات الإجابة في الاستعلام مثل Alexa و Siri، فإن محاولات البحث لتوسيع إمكانات النظام من أتمتة خدمة التعامل هي الآن وفيرة. ومع ذلك، فقد عثرت الأنظمة الأولية بسرعة على عدم كفاية في الاعتماد على تقنيات التصنيف البسيطة لتحقيق مهمة الأتمتة بشكل فعال. التحدي الرئيسي هو أن الحوار يتضمن في كثير من الأحيان التعقيد في نوايا المستخدم (أو أغراض) معتلقها، مع مراعاة التغيير التلقائي، ويصعب تتبعه. علاوة على ذلك، لم تعتبر مجموعات البيانات العامة هذه المضاعفات والشروح الدلالية العامة غير موجودة والتي قد تؤدي إلى مشكلة تسديدة صفرية. بدافع من ما ورد أعلاه، نقترح شبكة انتباه بيرت علم الملصقات (Laban) للكشف عن الصفر طلقة متعددة النوايا. نقوم أولا بتشفير كلمات الإدخال مع بيرت وبناء مساحة مضمنة تسمية من خلال النظر في دلالات مضمنة في ملصقات النوايا. ثم يتم تصنيف كلمة الإدخال بناء على أوزان الإسقاط على كل تضمين نية في هذه المساحة المضمنة. نظهر أنه تمتد بنجاح إلى عدد قليل من الإعدادات / الصفر، حيث يكون جزء من ملصقات النوايا غير مرئية في بيانات التدريب، من خلال مراعاة الدلالات أيضا في هذه الملصقات النية غير المرئية. تظهر النتائج التجريبية أن نهجنا قادر على اكتشاف العديد من الملصقات النية غير المرئية بشكل صحيح. كما أنه يحقق الأداء الحديثة في خمس مجموعات بيانات متعددة النوايا في الحالات العادية.
With the early success of query-answer assistants such as Alexa and Siri, research attempts to expand system capabilities of handling service automation are now abundant. However, preliminary systems have quickly found the inadequacy in relying on simple classification techniques to effectively accomplish the automation task. The main challenge is that the dialogue often involves complexity in user's intents (or purposes) which are multiproned, subject to spontaneous change, and difficult to track. Furthermore, public datasets have not considered these complications and the general semantic annotations are lacking which may result in zero-shot problem. Motivated by the above, we propose a Label-Aware BERT Attention Network (LABAN) for zero-shot multi-intent detection. We first encode input utterances with BERT and construct a label embedded space by considering embedded semantics in intent labels. An input utterance is then classified based on its projection weights on each intent embedding in this embedded space. We show that it successfully extends to few/zero-shot setting where part of intent labels are unseen in training data, by also taking account of semantics in these unseen intent labels. Experimental results show that our approach is capable of detecting many unseen intent labels correctly. It also achieves the state-of-the-art performance on five multi-intent datasets in normal cases.
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