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Register a new userKnowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation
المعرفة عززت الطاقة الجميلة للتعامل بشكل أفضل كيانات غير مرئية في توليد الحوار
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على الرغم من أن نماذج التدريب المسبق قد حققت نجاحا كبيرا في توليد الحوار، إلا أن أدائها ينخفض بشكل كبير عندما يحتوي المدخلات على كيان لا يظهر في مجموعات بيانات ما قبل التدريب والضبط (كيان غير مرئي). لمعالجة هذه المشكلة، تستفيد الأساليب الحالية لقاعدة المعرفة الخارجية لتوليد الاستجابات المناسبة. في العالم الحقيقي العملي، قد لا يتم تضمين الكيان من قبل قاعدة المعارف أو تعاني من دقة استرجاع المعرفة. للتعامل مع هذه المشكلة، بدلا من إدخال قاعدة المعرفة كإدخال، نقوم بإجبار النموذج على تعلم التمثيل الدلالي الأفضل من خلال التنبؤ بالمعلومات في قاعدة المعرفة، فقط بناء على سياق الإدخال. على وجه التحديد، بمساعدة قاعدة المعرفة، نقدم هدفين تدريبين مساعدين: 1) تفسير الكلمة الملثمين، والتي تخنق معنى الكيان الملثمين بالنظر إلى السياق؛ 2) توليد Hypernym، الذي يتنبأ فرق الكيان بناء على السياق. نتائج التجربة على اثنين من الحوار كوربوس تحقق من فعالية أساليبنا تحت كلا المعرفة والإعدادات المتاحة وغير متوفرة.
Although pre-training models have achieved great success in dialogue generation, their performance drops dramatically when the input contains an entity that does not appear in pre-training and fine-tuning datasets (unseen entity). To address this issue, existing methods leverage an external knowledge base to generate appropriate responses. In real-world practical, the entity may not be included by the knowledge base or suffer from the precision of knowledge retrieval. To deal with this problem, instead of introducing knowledge base as the input, we force the model to learn a better semantic representation by predicting the information in the knowledge base, only based on the input context. Specifically, with the help of a knowledge base, we introduce two auxiliary training objectives: 1) Interpret Masked Word, which conjectures the meaning of the masked entity given the context; 2) Hypernym Generation, which predicts the hypernym of the entity based on the context. Experiment results on two dialogue corpus verify the effectiveness of our methods under both knowledge available and unavailable settings.
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