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Register a new userMeta-Learning for Domain Generalization in Semantic Parsing
التعلم التعريف لتعميم المجال في التحليل الدلالي
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لقد تم الاعتراف بأهمية بناء المحللين الدلاليين الذين يمكن تطبيقهم على مجالات جديدة وإنشاء برامج غير مرئية في التدريب لفترة طويلة، وقد تم الاعتراف لفترة طويلة، وتصبح مجموعات البيانات اختبار الأداء خارج النطاق متاحة بشكل متزايد. ومع ذلك، فقد تم تخصيص اهتمام ضئيل أو معدوني لتعلم الخوارزميات أو الأهداف التي تعزز تعميم المجال، حيث تعتمد جميع الأساليب الموجودة تقريبا على التعلم المعياري الإشرافي. في هذا العمل، نستخدم إطارا للتعلم من التعريف الذي يستهدف تعميم المجال الصفرية للتحليل الدلالي. نحن نطبق خوارزمية التدريب النموذجية المرجعية التي تحاكي تحليل اللقطة الصفرية من خلال بناء القطار الافتراضي ومجموعات الاختبار من مجالات Disfoint. يستحق الهدف التعلم عن الحدس الذي يجب عليه اتخاذ خطوات التدرج التي تعمل على تحسين أداء مجال المصدر أيضا على تحسين أداء المجال المستهدف، وبالتالي تشجيع المحلل المحلل على تعميم المجالات المستهدفة غير المرئية. النتائج التجريبية على (الإنجليزية) عن مجموعات البيانات العنكبوت والصينية الصينية تظهر أن هدف التعلم التلوي يعزز بشكل كبير أداء محلل الأساس.
The importance of building semantic parsers which can be applied to new domains and generate programs unseen at training has long been acknowledged, and datasets testing out-of-domain performance are becoming increasingly available. However, little or no attention has been devoted to learning algorithms or objectives which promote domain generalization, with virtually all existing approaches relying on standard supervised learning. In this work, we use a meta-learning framework which targets zero-shot domain generalization for semantic parsing. We apply a model-agnostic training algorithm that simulates zero-shot parsing by constructing virtual train and test sets from disjoint domains. The learning objective capitalizes on the intuition that gradient steps that improve source-domain performance should also improve target-domain performance, thus encouraging a parser to generalize to unseen target domains. Experimental results on the (English) Spider and Chinese Spider datasets show that the meta-learning objective significantly boosts the performance of a baseline parser.
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Semantic parsing aims at translating natural language (NL) utterances onto machine-interpretable programs, which can be executed against a real-world environment. The expensive annotation of utterance-program pairs has long been acknowledged as a maj
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