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Register a new userBidirectional Domain Adaptation Using Weighted Multi-Task Learning
تكيف نطاق ثنائي الاتجاه باستخدام التعلم متعدد المهام المرجح
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لا يزال مخصصات المجال في التحليل النحوي تحديا كبيرا.نحن نعلم مسألة عدم توازن البيانات بين النطاق داخل المجال والخروج من النطاق يستخدم عادة للمشكلة.نحدد تكيف النطاق كمشكلة تعليمية متعددة المهام (MTL)، والتي تتيح لنا تدريب اثنين من المحللين، واحدة لكل منها الرئيسية.تظهر نتائجنا أن نهج MTL مفيد لنقش Treebank الأصغر.بالنسبة لأكبر Treebank، نحتاج إلى استخدام وزن الخسارة من أجل تجنب انخفاض في الأداء المهمة الفردية.من أجل تحديد درجة توهت البيانات، فإن اختلال البيانات بين مجطتين واختلافات المجال تؤثر على النتائج، ونحن نقوم أيضا بتجربة اثنين من Treebanks غير المتوازن داخل المجال وإظهار أن وزن الخسارة يحسن أيضا الأداء في إعداد المجال.نظرا لارتداء الخسارة في MTL، يمكننا تحسين النتائج لكل من المحللين.
Domain adaption in syntactic parsing is still a significant challenge. We address the issue of data imbalance between the in-domain and out-of-domain treebank typically used for the problem. We define domain adaptation as a Multi-task learning (MTL) problem, which allows us to train two parsers, one for each do-main. Our results show that the MTL approach is beneficial for the smaller treebank. For the larger treebank, we need to use loss weighting in order to avoid a decrease in performance be-low the single task. In order to determine towhat degree the data imbalance between two domains and the domain differences affect results, we also carry out an experiment with two imbalanced in-domain treebanks and show that loss weighting also improves performance in an in-domain setting. Given loss weighting in MTL, we can improve results for both parsers.
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