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تحليل الدلالات المستندة إلى الذاكرة
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الملخص نقدم نموذجا يستند إلى الذاكرة للتحليل الدلالي المعتمد على السياق.تركز النهج السابقة على تمكين وحدة فك الترميز لنسخ أو تعديل التحليل من الكلام السابق، على افتراض وجود تبعية بين الحواجز الحالية والسابقة.في هذا العمل، نقترح تمثيل معلومات سياقية باستخدام ذاكرة خارجية.نحن نتعلم وحدة تحكم ذاكرة السياق التي تدير الذاكرة عن طريق الحفاظ على المعنى التراكمي لإعلام المستخدمين المتسلسلين.نقيم نهجنا على ثلاثة معايير تحليل الدلالات.تظهر النتائج التجريبية أن طرازنا يمكن أن يقوم بتحسين معالجة المعلومات التي تعتمد على السياق وتظهر الأداء المحسن دون استخدام أجهزة فك تشفير المهام الخاصة.
Abstract We present a memory-based model for context- dependent semantic parsing. Previous approaches focus on enabling the decoder to copy or modify the parse from the previous utterance, assuming there is a dependency between the current and previous parses. In this work, we propose to represent contextual information using an external memory. We learn a context memory controller that manages the memory by maintaining the cumulative meaning of sequential user utterances. We evaluate our approach on three semantic parsing benchmarks. Experimental results show that our model can better process context-dependent information and demonstrates improved performance without using task-specific decoders.
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