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Register a new userProof Net Structure for Neural Lambek Categorial Parsing
دليل صافي إثبات التحليل الصلب ل Lambek العصبي
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في هذه الورقة، نقدم أول محلل إحصائي لغوي Lambek الصلب (LCG)، وهي منظماتية نحوية هي طريقة الإثبات الرسومية المعروفة باسم Nets Proof * قابلة للتطبيق.يشتمل محللنا على هيكل صافي مقاوم للقيود والقيود في نظام يعتمد على شبكات الانتباه الذاتي عبر عناصر نموذجية جديدة.تظهر تجاربنا على Corpus LCG English أن دمج هيكل الرسم البياني المصطلح مفيد للنموذج، مما يحسن كل من دقة التحليل والتغطية.علاوة على ذلك، فإننا نستمد وظائف الخسائر الرواية من خلال التعبير عن قيود صافي دليل على أنها وظائف مختلفة لإخراج النماذج لدينا، مما يتيح لنا تدريب محللنا دون اشتقامات في الحقيقة الأرضية.
In this paper, we present the first statistical parser for Lambek categorial grammar (LCG), a grammatical formalism for which the graphical proof method known as *proof nets* is applicable. Our parser incorporates proof net structure and constraints into a system based on self-attention networks via novel model elements. Our experiments on an English LCG corpus show that incorporating term graph structure is helpful to the model, improving both parsing accuracy and coverage. Moreover, we derive novel loss functions by expressing proof net constraints as differentiable functions of our model output, enabling us to train our parser without ground-truth derivations.
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