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Register a new userCompositional Generalization via Semantic Tagging
التعميم التركيبي عبر العلامات الدلالية
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على الرغم من تطبيق نماذج التسلسل العصبي للتسلسل بنجاح على التحليل الدلالي، إلا أنها تفشل في التعميم التركيبي، أي أنها غير قادرة على التعميم بشكل منهجي لتركيبات غير مرئية من مكونات المشاهدة. بدافع من التحليل الدلالي التقليدي حيث يتم احتساب التركيز بشكل صريح من قبل النحو الرمزي، نقترح إطار فك التشفير الجديد الذي يحافظ على التعبير عن النماذج والعمومية من نماذج التسلسل إلى التسلسل مع تضم محاذاة على غرار المعجم ومعالجة المعلومات المنفذة. على وجه التحديد، نقوم بتحلل فك التشفير في مرحلتين حيث يتم وضع علامة على حامل الإدخال أولا مع رموز الدلالية التي تمثل معنى الكلمات الفردية، ثم يتم استخدام نموذج تسلسل إلى تسلسل للتنبؤ بتصميم تمثيل المعنى النهائي على الكلام والعلامة المتوقعة تسلسل. النتائج التجريبية على ثلاث مجموعات بيانات تحليل الدلالات توضح أن النهج المقترح يحسن باستمرار التعميم التركيبي عبر الهندسة النموذجية والنطاقات والإضفاءات الدلالية.
Although neural sequence-to-sequence models have been successfully applied to semantic parsing, they fail at compositional generalization, i.e., they are unable to systematically generalize to unseen compositions of seen components. Motivated by traditional semantic parsing where compositionality is explicitly accounted for by symbolic grammars, we propose a new decoding framework that preserves the expressivity and generality of sequence-to-sequence models while featuring lexicon-style alignments and disentangled information processing. Specifically, we decompose decoding into two phases where an input utterance is first tagged with semantic symbols representing the meaning of individual words, and then a sequence-to-sequence model is used to predict the final meaning representation conditioning on the utterance and the predicted tag sequence. Experimental results on three semantic parsing datasets show that the proposed approach consistently improves compositional generalization across model architectures, domains, and semantic formalisms.
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