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Register a new userPre-train or Annotate? Domain Adaptation with a Constrained Budget
قبل القطار أو التعليق؟تكيف المجال مع ميزانية مقيدة
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أظهر العمل الحديث أن نماذج لغة التدريب المسبق التدريبية يمكن أن تعزز الأداء عند التكيف إلى مجال جديد. ومع ذلك، فإن التكاليف المرتبطة بالتدريب المسبق رفع سؤال مهم: بالنظر إلى ميزانية ثابتة، ما هي الخطوات التي يجب أن يستغرق ممارس NLP لتعظيم الأداء؟ في هذه الورقة، نقوم بدراسة تكيف المجال بموجب قيود الموازنة، ونهجها كمشكلة اختيار العملاء بين شروح البيانات والتدريب المسبق. على وجه التحديد، نقيس تكلفة التوضيحية لثلاث مجموعات بيانات نصية إجرائية وتكلفة ما قبل التدريب من ثلاث نماذج لغوية داخل المجال. ثم نقيم فائدة مجموعات مختلفة من التدريب المسبق والتشريح بالبيانات بموجب قيود ميزانية متفاوتة لتقييم الاستراتيجية التي تعمل بشكل أفضل. نجد أنه بالنسبة للميزانيات الصغيرة، فإن إنفاق جميع الأموال على التوضيحية يؤدي إلى أفضل أداء؛ بمجرد أن تصبح الميزانية كبيرة بما فيه الكفاية، يعمل مزيج من شرح البيانات والتدريب المسبق في المجال على النحو الأمثل. لذلك نقترح أن تكون شروط التعريف الخاصة بالبيانات الخاصة بمهارات العمل يجب أن تكون جزءا من استراتيجية اقتصادية عند تكييف نموذج NLP إلى مجال جديد.
Recent work has demonstrated that pre-training in-domain language models can boost performance when adapting to a new domain. However, the costs associated with pre-training raise an important question: given a fixed budget, what steps should an NLP practitioner take to maximize performance? In this paper, we study domain adaptation under budget constraints, and approach it as a customer choice problem between data annotation and pre-training. Specifically, we measure the annotation cost of three procedural text datasets and the pre-training cost of three in-domain language models. Then we evaluate the utility of different combinations of pre-training and data annotation under varying budget constraints to assess which combination strategy works best. We find that, for small budgets, spending all funds on annotation leads to the best performance; once the budget becomes large enough, a combination of data annotation and in-domain pre-training works more optimally. We therefore suggest that task-specific data annotation should be part of an economical strategy when adapting an NLP model to a new domain.
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Transformer-based pre-trained language models have significantly improved the performance of various natural language processing (NLP) tasks in the recent years. While effective and prevalent, these models are usually prohibitively large for resource
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