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Register a new userGraph-Based Decoding for Task Oriented Semantic Parsing
فك التشفير المستنتمي الرسم البياني لتحليل المهام المنحى الدلالي
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النموذج المهيمن للتحلل الدلالي في السنوات الأخيرة هو صياغة تحليل كمركز تسلسل إلى تسلسل، وتوليد تنبؤات مع فك تراجع التسلسل التلقائي.في هذا العمل، نستكشف نموذجا بديلا.نقوم بصياغة تحليل دلالي كهامة تحليل التبعية، وتطبيق تقنيات فك التشفير المستندة إلى الرسم البياني المتقدمة لتحليل النحوي.نحن نقارن مختلف تقنيات فك التشفير بالنظر إلى نفس التشفير المحول المدرب مسبقا في أفضل مجموعة البيانات، بما في ذلك الإعدادات التي تكون فيها بيانات التدريب محدودة أو تحتوي على أمثلة مشروح جزئيا فقط.نجد أن نهجنا القائم على الرسم البياني لدينا هو تنافسي مع فك ترميز الترميز على الإعداد المعياري، ويقدم تحسينات كبيرة في كفاءة البيانات والإعدادات حيث تتوفر البيانات المشروح جزئيا.
The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders. In this work, we explore an alternative paradigm. We formulate semantic parsing as a dependency parsing task, applying graph-based decoding techniques developed for syntactic parsing. We compare various decoding techniques given the same pre-trained Transformer encoder on the TOP dataset, including settings where training data is limited or contains only partially-annotated examples. We find that our graph-based approach is competitive with sequence decoders on the standard setting, and offers significant improvements in data efficiency and settings where partially-annotated data is available.
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