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Register a new userMAD-G: Multilingual Adapter Generation for Efficient Cross-Lingual Transfer
جنون G: جيل محول متعدد اللغات للتحويل الفعال عبر اللغات
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لقد ظهرت وحدات محول كوسيلة فعالة من المعلمات لتخصص التشفير المسبق على المجالات الجديدة. استفادت محولات متعددة اللغات بشكل كبير (MMTS) بشكل خاص من التدريب الإضافي للمحولات الخاصة باللغة. ومع ذلك، فإن هذا النهج ليس قابلا للتطبيق بالنسبة للغالبية العظمى من اللغات، بسبب القيود في حجم الشقوق أو حساب الميزانيات. في هذا العمل، نقترح جنون G (جيل محول متعدد اللغات)، الذي يولد محولات لغة محلية من تمثيلات اللغة بناء على الميزات النموذجية. على عكس العمل السابق، يتيح نهجنا المجنون بوقتنا وفعال الفضاء (1) تبادل المعرفة اللغوية عبر اللغات و (2) استنتاج صفرية عن طريق توليد محولات لغة للغات غير المرئية. نحن نقيم بدقة جنون G في النقل الصفر - نقل عبر اللغات على علامة جزء من الكلام، وتحليل التبعية، والاعتراف كيان المسمى. أثناء تقديم (1) تحسين كفاءة ضبط الدقيقة (1) من خلال عامل حوالي 50 في تجاربنا)، (2) ميزانية معلمة أصغر، و (3) زيادة تغطية اللغة، لا تزال جنون جي تنافسية مع أساليب أكثر تكلفة للغة تدريب محول محدد في جميع اللوحة. علاوة على ذلك، فإنه يوفر فوائد كبيرة لغات الموارد المنخفضة، لا سيما في مهمة NER في لغات أفريقية منخفضة الموارد. أخيرا، نوضح أن أداء نقل جنون جي يمكن تحسينه عبر: (1) التدريب متعدد المصادر، أي، من خلال توليد ومجتمعة محولات لغات متعددة مع بيانات التدريب الخاصة بمهام المهام المتاحة؛ و (2) عن طريق مزيد من ضبط محولات جنون G للغات ولغات مع بيانات أحادية الأونلينغ.
Adapter modules have emerged as a general parameter-efficient means to specialize a pretrained encoder to new domains. Massively multilingual transformers (MMTs) have particularly benefited from additional training of language-specific adapters. However, this approach is not viable for the vast majority of languages, due to limitations in their corpus size or compute budgets. In this work, we propose MAD-G (Multilingual ADapter Generation), which contextually generates language adapters from language representations based on typological features. In contrast to prior work, our time- and space-efficient MAD-G approach enables (1) sharing of linguistic knowledge across languages and (2) zero-shot inference by generating language adapters for unseen languages. We thoroughly evaluate MAD-G in zero-shot cross-lingual transfer on part-of-speech tagging, dependency parsing, and named entity recognition. While offering (1) improved fine-tuning efficiency (by a factor of around 50 in our experiments), (2) a smaller parameter budget, and (3) increased language coverage, MAD-G remains competitive with more expensive methods for language-specific adapter training across the board. Moreover, it offers substantial benefits for low-resource languages, particularly on the NER task in low-resource African languages. Finally, we demonstrate that MAD-G's transfer performance can be further improved via: (i) multi-source training, i.e., by generating and combining adapters of multiple languages with available task-specific training data; and (ii) by further fine-tuning generated MAD-G adapters for languages with monolingual data.
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