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Register a new userNeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints
فك التشفير العصبي: (الأمم المتحدة) يشرف على توليد النص العصبي مع قيود المنطق المسند
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غالبا ما يتطلب جيل النص الشرطي القيود المعجمية، أي الكلمات التي يجب أو لا ينبغي إدراجها في نص الإخراج. في حين أن الوصفة المهيمنة لجيل النظام الشرطي كانت نماذج لغوية متماثلة على نطاق واسع يتم تصويرها على بيانات التدريب الخاصة بمهام المهام، فإن مثل هذه النماذج لا تتعلم اتباع القيود الأساسية بشكل موثوق، حتى عند الإشراف على كميات كبيرة من الأمثلة الخاصة بمهام المهام وبعد نقترح فك التشفير العصبي، خوارزمية بسيطة ولكنها فعالة تمكن نماذج اللغة العصبية - تحت إشراف أو لا - لتوليد نص بطلاقة مع مرضية القيود المعقدة المعقدة. نهجنا قوي بعد كفاءة. يتعامل مع أي مجموعة من القيود المعجمية المعبرة تحت المنطق المسند، في حين أن وقت التشغيل مقاربها يعادل البحث عن شعاع التقليدية. تظهر النتائج التجريبية على أربعة معايير أن فك التشفير العصبي تتفوق على النهج السابقة، بما في ذلك الخوارزميات التي تتعامل مع مجموعة فرعية من قيودنا. علاوة على ذلك، نجد أن النماذج غير الخاضعة للكشف عن فك التشفير العصبي في كثير من الأحيان تفوق النماذج الخاضعة للإشراف مع فك التشفير التقليدي، حتى عندما تستند الأخير إلى شبكات أكبر بكثير. تشير نتائجنا إلى حد الشبكات العصبية واسعة النطاق لتوليد القابل للتحكم بالقلق ووعد خوارزميات وقت الاستقدمية.
Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text. While the dominant recipe for conditional text generation has been large-scale pretrained language models that are finetuned on the task-specific training data, such models do not learn to follow the underlying constraints reliably, even when supervised with large amounts of task-specific examples. We propose NeuroLogic Decoding, a simple yet effective algorithm that enables neural language models -- supervised or not -- to generate fluent text while satisfying complex lexical constraints. Our approach is powerful yet efficient. It handles any set of lexical constraints that is expressible under predicate logic, while its asymptotic runtime is equivalent to conventional beam search. Empirical results on four benchmarks show that NeuroLogic Decoding outperforms previous approaches, including algorithms that handle a subset of our constraints. Moreover, we find that unsupervised models with NeuroLogic Decoding often outperform supervised models with conventional decoding, even when the latter is based on considerably larger networks. Our results suggest the limit of large-scale neural networks for fine-grained controllable generation and the promise of inference-time algorithms.
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