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Register a new userWhen does Further Pre-training MLM Help? An Empirical Study on Task-Oriented Dialog Pre-training
متى تساعد MLM أو إعادة تدريب المزيد مسبقا؟دراسة تجريبية حول الحوار موجه نحو المهام قبل التدريب
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مزيد من النماذج اللغوية المسبقة للتدريب على البيانات داخل المجال (التدريب المسبق مسبقا، Dapt) أو البيانات ذات الصلة (TAME-APT-APTICTIVE، TAPT) قبل أن تؤدي إلى تحسين أداء المهام المصب.ومع ذلك، في نمذجة الحوار الموجهة نحو المهام، نلاحظ أن مزيد من الامتيازات التدريبية قبل التدريب لا تعزز دائما الأداء في مهمة المصب.نجد أن DIST مفيد في إعداد الموارد المنخفضة، ولكن نظرا لأن حجم بيانات ضبط الرصيف ينمو، يصبح DIST أقل فائدة أو حتى عديمة الفائدة، وتوسيع نطاق حجم بيانات Dapt لا يساعد.من خلال تحليل التشابه التمثيلي، نستنتج أن المزيد من البيانات الخاصة بالضبط بشكل جيد غلة تغيير أكبر في تمثيلات النموذج وبالتالي تقلل من تأثير التهيئة.
Further pre-training language models on in-domain data (domain-adaptive pre-training, DAPT) or task-relevant data (task-adaptive pre-training, TAPT) before fine-tuning has been shown to improve downstream tasks' performances. However, in task-oriented dialog modeling, we observe that further pre-training MLM does not always boost the performance on a downstream task. We find that DAPT is beneficial in the low-resource setting, but as the fine-tuning data size grows, DAPT becomes less beneficial or even useless, and scaling the size of DAPT data does not help. Through Representational Similarity Analysis, we conclude that more data for fine-tuning yields greater change of the model's representations and thus reduces the influence of initialization.
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