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Register a new userA Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation
إطار تعليمي ثلاث مراحل لتوليد الحوار المعرفي المنخفض
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أظهرت نماذج المحادثة العصبية إمكانات كبيرة تجاه توليد ردود بطلاقة وإمعلومات عن طريق إدخال معرفة خلفية خارجية. ومع ذلك، فمن الشائع بناء هذه الحوارات المدرجة في المعرفة، وعادة ما تؤدي النماذج الحالية بشكل سيء عند النقل إلى مجالات جديدة مع عينات تدريب محدودة. لذلك، فإن بناء نظام حوار مدرج في المعرفة بموجب إعداد الموارد المنخفضة هو قضية حاسمة لا تزال. في هذه الورقة، نقترح إطارا لتعليم تعليمي رواية ثلاث مراحل يستند إلى التعلم الإشرافه ضعيف يفيد من الحوارات على نطاق واسع وقاعدة المعرفة غير المنظمة. للتعاون بشكل أفضل مع هذا الإطار، نضع متغير من المحولات مع فك فك التشفير التي تسهل التعلم المنطلق لتوليد الاستجابة وإدماج المعرفة. تشير نتائج التقييم إلى معيارين إلى أن نهجنا يمكن أن يتفوق على أساليب حديثة أخرى مع بيانات تدريب أقل، وحتى في سيناريو الموارد الصفرية، فإن نهجنا لا يزال ينفذ جيدا.
Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge. Nevertheless, it is laborious to construct such knowledge-grounded dialogues, and existing models usually perform poorly when transfer to new domains with limited training samples. Therefore, building a knowledge-grounded dialogue system under the low-resource setting is a still crucial issue. In this paper, we propose a novel three-stage learning framework based on weakly supervised learning which benefits from large scale ungrounded dialogues and unstructured knowledge base. To better cooperate with this framework, we devise a variant of Transformer with decoupled decoder which facilitates the disentangled learning of response generation and knowledge incorporation. Evaluation results on two benchmarks indicate that our approach can outperform other state-of-the-art methods with less training data, and even in zero-resource scenario, our approach still performs well.
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