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Register a new userAn Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling
إطار تعليمي صريح ومشروع على نطاق واسع لتصنيف عدد قليل من الطلقات وملء الفتحة
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تعد تصنيف النوايا (IC) وملء الفتحات (SF) لبنات بناء مهمة في أنظمة الحوار الموجهة نحو المهام. هذه المهامتين مرتبطان ارتباطا وثيقا ويمكن أن تزدهر بعضهما البعض. نظرا لأن عدد قليل فقط من الكلام، يمكن استخدامها لتحديد النوايا والفتحات الجديدة الناشئة، وغالبا ما تحدث مشكلة ندرة البيانات عند تنفيذ IC و SF. ومع ذلك، فإن عدد قليل من نماذج IC / SF تعمل بشكل جيد عندما يكون عدد عينات التدريب لكل فئة صغيرة جدا. في هذه الورقة، نقترح إطارا تعليميا مشتركا متميزا ومشروعا مختلفا ومشروعا للإشراف من أجل تصنيف قلة الطابع وملء الفتحة. أبرزها هي كما يلي. (1) يقوم النموذج بإجراء عروض النية والفخان عبر التفاعلات ثنائية الاتجاه، ويمتد الشبكة النموذجية لتحقيق التعلم الصريح المشترك، والذي يضمن أن مهام IC و SF يمكن أن تعزز بعضها البعض. (2) يتكامل النموذج مع التعلم المتعري الخاضع للإشراف، مما يضمن سحب العينات من نفس الفصل معا ويتم دفع عينات من فئات مختلفة. بالإضافة إلى ذلك، يتبع النموذج بطريقة غير شائعة ولكن عملية لبناء الحلقة، والتي تتخلص من الإعداد التقليدي مع طريقة ثابتة وإطلاق النار، وتسمح بموادات البيانات غير المتوازنة. تجارب واسعة على ثلاث مجموعات بيانات عامة تظهر أن نموذجنا يمكن أن يحقق أداء واعد.
Intent classification (IC) and slot filling (SF) are critical building blocks in task-oriented dialogue systems. These two tasks are closely-related and can flourish each other. Since only a few utterances can be utilized for identifying fast-emerging new intents and slots, data scarcity issue often occurs when implementing IC and SF. However, few IC/SF models perform well when the number of training samples per class is quite small. In this paper, we propose a novel explicit-joint and supervised-contrastive learning framework for few-shot intent classification and slot filling. Its highlights are as follows. (i) The model extracts intent and slot representations via bidirectional interactions, and extends prototypical network to achieve explicit-joint learning, which guarantees that IC and SF tasks can mutually reinforce each other. (ii) The model integrates with supervised contrastive learning, which ensures that samples from same class are pulled together and samples from different classes are pushed apart. In addition, the model follows a not common but practical way to construct the episode, which gets rid of the traditional setting with fixed way and shot, and allows for unbalanced datasets. Extensive experiments on three public datasets show that our model can achieve promising performance.
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Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce domain. In this paper, we explore retrieval-based methods for intent c
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