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Register a new userUnifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources
توحيد الدور الدلالي عبر اللغات مع الموارد اللغوية غير المتجانسة
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في حين أن تقنيات التبغيات المتبقية تجد نجاحا متزايدا في مجموعة واسعة من مهام معالجة اللغة الطبيعية، فإن تطبيقها على الدورات الدلالية (SRL) كان محدودا بقوة من خلال حقيقة أن كل لغة تعتمد شكليها اللغوي الخاص بها، من Propbank من أجل أنظمة إنجليكزي للإسبانية و PDT-VALLEX لتشيك، في جملة أمور. في هذا العمل، نتعلم هذه المشكلة وتقديم نموذج موحد لأداء SRL عبر اللغات عبر الموارد اللغوية غير المتجانسة. يتعلم نموذجنا ضمنيا تعيين عالي الجودة من أجل الشكليات المختلفة عبر لغات متنوعة دون اللجوء إلى محاذاة Word وتقنيات الترجمة. نجد ذلك، ليس فقط نظامنا المتبادل لدينا تنافس مع الحالة الحالية للفن ولكنها قوية أيضا على سيناريوهات البيانات المنخفضة. من المثير للاهتمام، من المثير للاهتمام، نموذجنا الموحد قادر على التعليق الجملة في تمريرة واحدة إلى الأمام مع جميع المخزونات التي تم تدريبها عليها، وتوفير أداة لتحليل ومقارنة النظريات اللغوية عبر لغات مختلفة. نطلق سردنا ونموذجنا في https://github.com/sapienzanlp/unify-srl.
While cross-lingual techniques are finding increasing success in a wide range of Natural Language Processing tasks, their application to Semantic Role Labeling (SRL) has been strongly limited by the fact that each language adopts its own linguistic formalism, from PropBank for English to AnCora for Spanish and PDT-Vallex for Czech, inter alia. In this work, we address this issue and present a unified model to perform cross-lingual SRL over heterogeneous linguistic resources. Our model implicitly learns a high-quality mapping for different formalisms across diverse languages without resorting to word alignment and/or translation techniques. We find that, not only is our cross-lingual system competitive with the current state of the art but that it is also robust to low-data scenarios. Most interestingly, our unified model is able to annotate a sentence in a single forward pass with all the inventories it was trained with, providing a tool for the analysis and comparison of linguistic theories across different languages. We release our code and model at https://github.com/SapienzaNLP/unify-srl.
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