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Register a new userStyle Control for Schema-Guided Natural Language Generation
التحكم في النمط لتوليد اللغة الطبيعية الموجهة المخطط
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تركز توليد اللغة الطبيعية (NLG) لأنظمة الحوار الموجهة نحو المهام على توصيل محتوى معين بدقة، بطلاقة، وتطافق. في حين أن هذه السمات أمر حاسم للحوار الناجح، فمن المستحسن أيضا تحقيق أهداف أسلوبية محددة في وقت واحد، مثل طول الاستجابة، وجهة النظر، وصفي، والشعور، والشكل، والشكل، والتعاطف. في هذا العمل، نركز على التحكم الأسلطي والتقييم ل NLG الموجهة للمخطط، مع أهداف مشتركة لتحقيق السيطرة الدلالية واللحلية. نقوم بتجربة تفصيلية مع مختلف طرق الجيل التي تسيطر عليها نماذج اللغة المحددة مسبقا: على وجه التحديد، والتدريب الشرطي، والضبط الجميل الموجه، والكشف الموجهة. نناقش مزاياها والقيود الخاصة بهم، وتقييمها بمجموعة واسعة من مقاييس التقييم التلقائي والبشري. تظهر نتائجنا أنه في حين أن الدقة عالية النمط والصحة الدلالية أسهل في تحقيق المزيد من الأساليب المعرفة من المعالم مع التدريب الشرطي، فإن التحكم الأسطوري يمكن تحقيقه أيضا على أنماط معقدة أكثر دلالة تستخدم أساليب فك التشفير الموجودة على أساس التمييز. تشير النتائج أيضا إلى أن الطرق التي تعتبر أكثر قابلية للتطوير (مع ضبط المعلمات أقل فرط) وأن توليد سياق Disent Nastange والاختلافات الأسلوبية أكثر فعالية في تحقيق صحة دلالية ودقة أسلوب.
Natural Language Generation (NLG) for task-oriented dialogue systems focuses on communicating specific content accurately, fluently, and coherently. While these attributes are crucial for a successful dialogue, it is also desirable to simultaneously accomplish specific stylistic goals, such as response length, point-of-view, descriptiveness, sentiment, formality, and empathy. In this work, we focus on stylistic control and evaluation for schema-guided NLG, with joint goals of achieving both semantic and stylistic control. We experiment in detail with various controlled generation methods for large pretrained language models: specifically, conditional training, guided fine-tuning, and guided decoding. We discuss their advantages and limitations, and evaluate them with a broad range of automatic and human evaluation metrics. Our results show that while high style accuracy and semantic correctness are easier to achieve for more lexically-defined styles with conditional training, stylistic control is also achievable for more semantically complex styles using discriminator-based guided decoding methods. The results also suggest that methods that are more scalable (with less hyper-parameters tuning) and that disentangle context generation and stylistic variations are more effective at achieving semantic correctness and style accuracy.
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