خلال مرحلة التوصيل الدقيقة للتعلم، لا يزال المفردات المسبدة مسبقا دون تغيير، في حين يتم تحديث المعلمات النموذجية.المفردات الناتجة بناء على البيانات المحددة مسبقا هي فرعية نفسية للبيانات المصب عند وجود تناقض المجال.نقترح النظر في المفردات كمعلمة قابلة للتحسين، مما يسمح لنا بتحديث المفردات من خلال توسيعها مع المفردات المحددة للمجال بناء على إحصاء التكتيف.علاوة على ذلك، نحافظ على تضيير الكلمات المضافة من التجاوز إلى البيانات المصب عن طريق الاستفادة من المعرفة المستفادة من نموذج لغة مسبق مع مصطلح التنظيم.حققت طريقتنا تحسينات أداء ثابتة حول مجالات متنوعة (أي، العلوم الطبية الحيوية، علوم الكمبيوتر، الأخبار، والمراجعات).
During the fine-tuning phase of transfer learning, the pretrained vocabulary remains unchanged, while model parameters are updated. The vocabulary generated based on the pretrained data is suboptimal for downstream data when domain discrepancy exists. We propose to consider the vocabulary as an optimizable parameter, allowing us to update the vocabulary by expanding it with domain specific vocabulary based on a tokenization statistic. Furthermore, we preserve the embeddings of the added words from overfitting to downstream data by utilizing knowledge learned from a pretrained language model with a regularization term. Our method achieved consistent performance improvements on diverse domains (i.e., biomedical, computer science, news, and reviews).
References used
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