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Register a new userSelf-Training for Compositional Neural NLG in Task-Oriented Dialogue
التدريب الذاتي ل NLG العصبي التركيبي في الحوار الموجه نحو المهام
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عادة ما تتطلب النهج العصبية لتوليد اللغة الطبيعية في الحوار الموجه في المهام كميات كبيرة من بيانات التدريب المشروح لتحقيق أداء مرض، خاصة عند توليد المدخلات التركيبية. لمعالجة هذه المشكلة، نظهر أن التدريب الذاتي المعزز مع فك التشفير المقيد غلة مكاسب كبيرة في كفاءة البيانات على مجموعة بيانات الطقس التي توظف تمثيلات المعنى المتراكم. على وجه الخصوص، تشير تجاربنا إلى أن التدريب الذاتي مع فك التشفير المقيد يمكن أن تمكن نماذج التسلسل إلى التسلسل لتحقيق جودة مرضية باستخدام بيانات أقل من خمسة إلى عشرة أضعاف بيانات أقل من خط الأساس الخاضع للإشراف العادي؛ علاوة على ذلك، من خلال الاستفادة من النماذج المحددة، يمكن زيادة كفاءة البيانات إلى خمسين مرة. نؤكد النتائج التلقائية الرئيسية مع التقييمات البشرية وإظهار أنها تمتد إلى نسخة محسنة وتركيبية من DataSet E2E. والنتيجة النهائية هي نهج يجعل من الممكن تحقيق أداء مقبول على مهام NLG التركيبية باستخدام المئات بدلا من عشرات الآلاف من عينات التدريب.
Neural approaches to natural language generation in task-oriented dialogue have typically required large amounts of annotated training data to achieve satisfactory performance, especially when generating from compositional inputs. To address this issue, we show that self-training enhanced with constrained decoding yields large gains in data efficiency on a conversational weather dataset that employs compositional meaning representations. In particular, our experiments indicate that self-training with constrained decoding can enable sequence-to-sequence models to achieve satisfactory quality using vanilla decoding with five to ten times less data than with ordinary supervised baseline; moreover, by leveraging pretrained models, data efficiency can be increased further to fifty times. We confirm the main automatic results with human evaluations and show that they extend to an enhanced, compositional version of the E2E dataset. The end result is an approach that makes it possible to achieve acceptable performance on compositional NLG tasks using hundreds rather than tens of thousands of training samples.
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