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Register a new userConditional Poisson Stochastic Beams
عوارض ستوكاستيك مشروطة
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Beam Search هي استراتيجية فك التشفير الافتراضية للعديد من مهام توليد التسلسل في NLP. مجموعة من العناصر K-Best التقريبية التي تم إرجاعها بواسطة الخوارزمية هي ملخص مفيد للتوزيع للعديد من التطبيقات؛ ومع ذلك، عادة ما تظهر المرشحين تداخلا كبيرا وقد يعطي تقديرا متحيزين للغاية للتوقعات تحت نموذجنا. يمكن معالجة هذه المشكلات من خلال استخدام استراتيجيات فك تشفير الاستوكاستك. في هذا العمل، نقترح طريقة جديدة لتحويل البحث الشعاع في عملية عموقة: البحث الشرطي Poisson Stochastic Search. بدلا من اتخاذ تعظيم المجموعة في كل تكرار، ونحن نعمل عينة من المرشحين k دون استبدال وفقا لتصميم أخذ العينات poisson الشرطية. نعتبر ذلك بديلا أكثر طبيعية ل Kool et al. (2019) بحث شعاع ستوكاستيك (SBS). علاوة على ذلك، نوضح كيف يمكن استخدام العينات التي تم إنشاؤها تحت تصميم CPSBS لبناء مقدرات متسقة وعينة مجموعات متنوعة من نماذج التسلسل. في تجاربنا، نلاحظ CPSBS تنتج التباين المنخفض ومقدرات أكثر كفاءة من SBS، حتى إظهار التحسينات في إعدادات Enterpy عالية.
Beam search is the default decoding strategy for many sequence generation tasks in NLP. The set of approximate K-best items returned by the algorithm is a useful summary of the distribution for many applications; however, the candidates typically exhibit high overlap and may give a highly biased estimate for expectations under our model. These problems can be addressed by instead using stochastic decoding strategies. In this work, we propose a new method for turning beam search into a stochastic process: Conditional Poisson stochastic beam search. Rather than taking the maximizing set at each iteration, we sample K candidates without replacement according to the conditional Poisson sampling design. We view this as a more natural alternative to Kool et al. (2019)'s stochastic beam search (SBS). Furthermore, we show how samples generated under the CPSBS design can be used to build consistent estimators and sample diverse sets from sequence models. In our experiments, we observe CPSBS produces lower variance and more efficient estimators than SBS, even showing improvements in high entropy settings.
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