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Register a new userRethinking Perturbations in Encoder-Decoders for Fast Training
إعادة التفكير في الاضطرابات في فك ترميز التشفير للتدريب السريع
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نحن غالبا ما نستخدم الاضطرابات لتنظيم النماذج العصبية.بالنسبة للكشف عن المشفر العصبي، طبقت الدراسات السابقة أخذ العينات المجدولة (بنغيو وآخرون.، 2015) والاضطرابات الخصومة (SATO et al.، 2019) كشراءات ولكن هذه الطرق تتطلب وقتا حسابيا كبيرا.وبالتالي، فإن هذه الدراسة تعالج مسألة ما إذا كانت هذه الأساليب فعالة بما يكفي لتدريب الوقت.قارنا العديد من الاضطرابات في مشاكل التسلسل إلى التسلسل فيما يتعلق بالوقت الحاسوبية.تظهر النتائج التجريبية أن التقنيات البسيطة مثل Hold Dropout (GAL و GHAHRAMANI، 2016) واستبدال عشوائي من الرموز المدخلات يحققون درجات قابلة للمقارنة (أو أفضل) إلى الاضطرابات المقترحة مؤخرا، على الرغم من أن هذه الطرق البسيطة أسرع.
We often use perturbations to regularize neural models. For neural encoder-decoders, previous studies applied the scheduled sampling (Bengio et al., 2015) and adversarial perturbations (Sato et al., 2019) as perturbations but these methods require considerable computational time. Thus, this study addresses the question of whether these approaches are efficient enough for training time. We compare several perturbations in sequence-to-sequence problems with respect to computational time. Experimental results show that the simple techniques such as word dropout (Gal and Ghahramani, 2016) and random replacement of input tokens achieve comparable (or better) scores to the recently proposed perturbations, even though these simple methods are faster.
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