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Register a new userSpoken Language Understanding for Task-oriented Dialogue Systems with Augmented Memory Networks
فهم اللغة المنطوقة لأنظمة الحوار الموجهة نحو المهام مع شبكات الذاكرة المعزز
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فهم اللغة المنطوقة، عادة بما في ذلك اكتشاف النوايا وملء الفتحات، هو مكون أساسي لبناء نظام حوار منطوق. تظهر الأبحاث الحديثة نتائج واعدة من خلال التعلم المشترك بين هذين المهامتين بناء على حقيقة أن ملء الفتحة والكشف عن النوايا تشارك المعرفة الدلالية. علاوة على ذلك، فإن آلية الاهتمام تعزز التعلم المشترك لتحقيق نتائج أحدث من الفن. ومع ذلك، فإن نماذج التعلم المشتركة الحالية تتجاهل الحقائق المهمة التالية: 1. لا يتم تتبع سياق فتحة طويلة الأجل بشكل فعال، وهو أمر حاسم لملء الفتحات المستقبلية. 2. يمكن أن تكون الفتحة وعلامات الكشف عن النية مجزية بشكل متبادل، ولكن التفاعل ثنائي الاتجاه بين ملء الفتحات والكشف عن النوايا لا يزال نادرا ما تم استكشافه. في هذه الورقة، نقترح نهجا جديدا لنموذج سياق فتحة طويلة الأجل واستخدام العلاقة الدلالية بالكامل بين الفتحات والمحالة. نعتمد شبكة الذاكرة ذات القيمة الرئيسية لنموذج سياق الفتحة ديناميكيا وتتبع علامات فتحة أكثر أهمية فك شفرة من قبل، والتي يتم تغذيتها بعد ذلك في وحدة فك التشفير الخاصة بنا للحصول على علامات الفتحة. علاوة على ذلك، يتم استخدام معلومات الذاكرة الدائرية لأداء الكشف عن النية، وتحسين المهام المتبادلة من خلال التحسين العالمي. تظهر التجارب على معيار ATIS و SHITS DataSets أن نموذجنا يحقق أداء حديثة وتفوق على طرق أخرى، خاصة بالنسبة لمهمة ملء الفتحة.
Spoken language understanding, usually including intent detection and slot filling, is a core component to build a spoken dialog system. Recent research shows promising results by jointly learning of those two tasks based on the fact that slot filling and intent detection are sharing semantic knowledge. Furthermore, attention mechanism boosts joint learning to achieve state-of-the-art results. However, current joint learning models ignore the following important facts: 1. Long-term slot context is not traced effectively, which is crucial for future slot filling. 2. Slot tagging and intent detection could be mutually rewarding, but bi-directional interaction between slot filling and intent detection remains seldom explored. In this paper, we propose a novel approach to model long-term slot context and to fully utilize the semantic correlation between slots and intents. We adopt a key-value memory network to model slot context dynamically and to track more important slot tags decoded before, which are then fed into our decoder for slot tagging. Furthermore, gated memory information is utilized to perform intent detection, mutually improving both tasks through global optimization. Experiments on benchmark ATIS and Snips datasets show that our model achieves state-of-the-art performance and outperforms other methods, especially for the slot filling task.
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