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Register a new userTwo Heads are Better than One? Verification of Ensemble Effect in Neural Machine Translation
اثنين من رؤساء هي أفضل من واحدة؟التحقق من تأثير الفرقة في الترجمة الآلية العصبية
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في مجال معالجة اللغة الطبيعية، من المعروف أن الكفارات فعالة في تحسين الأداء.تحلل هذه الورقة كيف تؤثر فرقة نماذج الترجمة الآلية العصبية (NMT) على تحسين الأداء من خلال تصميم مختلف الإعدادات التجريبية (I.E.، Intra-، Inter-Ertern-Erbergble، وغير غير التقاعد).لفحص متعمق، نقوم بتحليل كل طريقة فرقة فيما يتعلق بالعديد من جوانب مثل نماذج الاهتمام المختلفة واستراتيجيات VOCAB.تظهر النتائج التجريبية أن الكوغرات لا يؤدي دائما إلى زيادة الأداء وتقديم النتائج السلبية الجديرة بالملاحظة.
In the field of natural language processing, ensembles are broadly known to be effective in improving performance. This paper analyzes how ensemble of neural machine translation (NMT) models affect performance improvement by designing various experimental setups (i.e., intra-, inter-ensemble, and non-convergence ensemble). To an in-depth examination, we analyze each ensemble method with respect to several aspects such as different attention models and vocab strategies. Experimental results show that ensembling is not always resulting in performance increases and give noteworthy negative findings.
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