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Register a new userUnsupervised Domain Adaptation Method with Semantic-Structural Alignment for Dependency Parsing
طريقة تكيف المجال غير المزعجة مع محاذاة الهيكلية الدلالية لتحليل التبعية
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تحليل التبعية عبر المجال غير الخاضع للإكمال هو إنجاز تكيف مجال تحليل التبعية دون استخدام البيانات المسمى في المجال المستهدف. غالبا ما تكون الأساليب الحالية من نوع التوضيح الزائفة، والتي تنشئ البيانات من خلال التوضيح الذاتي للنموذج الأساسي وأداء التدريب التكراري. ومع ذلك، فشلت هذه الطرق في النظر في تغيير هيكل النموذج لتكييف المجال. بالإضافة إلى ذلك، لا يمكن استغلال المعلومات الهيكلية الواردة في النص بالكامل. لعلاج هذه العيوب، نقترح محلل التبعية التبعية للتكيف مع بنية دلالات (SSADP)، التي تنجز تحليلات التبعية عبر المجال غير الخاضعة للكشف دون الاعتماد على التوضيح الزائفة أو اختيار البيانات. على وجه الخصوص، نقوم بتصميم اثنين من النازعين ميزة لاستخراج الميزات الدلالية والهيكلية على التوالي. لكل نوع من الميزات، يتم استخدام طريقة تكيف الميزة المقابلة لتحقيق تكيف المجال لمواءمة توزيع المجال، والتي تعزز بشكل فعال إمكانية نقل المجال المتقاطع بشكل فعال للنموذج. نحن نقوم بالتحقق من فعالية طرازنا عن طريق إجراء تجارب على Codt1 و CTB9 على التوالي، وتظهر النتائج أن نموذجنا يمكن أن يحقق تحسين أداء ثابتا. علاوة على ذلك، نتحقق من قدرة نقل الهيكل النموذج المقترح عن طريق إدخال اختبار Weisfeiler-Lehman.
Unsupervised cross-domain dependency parsing is to accomplish domain adaptation for dependency parsing without using labeled data in target domain. Existing methods are often of the pseudo-annotation type, which generates data through self-annotation of the base model and performing iterative training. However, these methods fail to consider the change of model structure for domain adaptation. In addition, the structural information contained in the text cannot be fully exploited. To remedy these drawbacks, we propose a Semantics-Structure Adaptative Dependency Parser (SSADP), which accomplishes unsupervised cross-domain dependency parsing without relying on pseudo-annotation or data selection. In particular, we design two feature extractors to extract semantic and structural features respectively. For each type of features, a corresponding feature adaptation method is utilized to achieve domain adaptation to align the domain distribution, which effectively enhances the unsupervised cross-domain transfer capability of the model. We validate the effectiveness of our model by conducting experiments on the CODT1 and CTB9 respectively, and the results demonstrate that our model can achieve consistent performance improvement. Besides, we verify the structure transfer ability of the proposed model by introducing Weisfeiler-Lehman Test.
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