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Register a new userControlling Dialogue Generation with Semantic Exemplars
السيطرة على جيل الحوار مع exemplars الدلالي
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إن أنظمة الحوار المحددة مع نماذج لغة كبيرة تولد ردود متماسكة محليا، ولكن تفتقر إلى السيطرة الجميلة على الردود اللازمة لتحقيق أهداف محددة.تتميز طريقة واعدة للسيطرة على جيل الاستجابة جيل يستند إلى Exemplar، حيث تحرير النماذج تحرير الردود المثالية التي يتم استرجاعها من بيانات التدريب، أو مكتوبة يدويا إلى أهداف مستوى الخطاب استراتيجيا، لتناسب سياقات حوار جديدة.نقدم نموذج توليد حوار يستند إلى Exemplar، EDGE، يستخدم الإطارات الدلالية الموجودة في ردود Exemplar لتوجيه جيل الاستجابة.نظير على أن السيطرة على توليد الحوار على أساس الإطارات الدلالية من النماذج يحسن تماسك الردود التي تم إنشاؤها، مع الحفاظ على المعنى الدلالي وأهداف المحادثة الموجودة في ردود مثالية.
Dialogue systems pretrained with large language models generate locally coherent responses, but lack fine-grained control over responses necessary to achieve specific goals. A promising method to control response generation is exemplar-based generation, in which models edit exemplar responses that are retrieved from training data, or hand-written to strategically address discourse-level goals, to fit new dialogue contexts. We present an Exemplar-based Dialogue Generation model, EDGE, that uses the semantic frames present in exemplar responses to guide response generation. We show that controlling dialogue generation based on the semantic frames of exemplars improves the coherence of generated responses, while preserving semantic meaning and conversation goals present in exemplar responses.
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