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Register a new userEverything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction
كل شيء هو كل ما يتطلبه الأمر: استراتيجية متعددة الأحبائية لاستخراج المعلومات الصفرية عبر اللغات
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يصف استخراج المعلومات عبر اللغات الصفرية (IE) بناء نموذج IE لبعض اللغة المستهدفة، بالنظر إلى التعليقات التوضيحية القائمة حصريا في لغة أخرى، عادة باللغة الإنجليزية. في حين أن تقدم اللوائح المتعددة اللغات المحددة مسبقا يشير إلى تفاؤل سهلة للقطار على اللغة الإنجليزية، وتشغيل أي لغة ""، نجد من خلال استكشاف شامل وتمديد التقنيات التي تقودها مجموعة من الأساليب، الجديدة القديمة، إلى أداء أفضل من أي استراتيجية واحدة عبر اللغات على وجه الخصوص. نستكشف التقنيات بما في ذلك إسقاط البيانات والتدريب الذاتي، وكيف تأثير المشفرات المختلفة مسبقا تأثيرها. نستخدم English-to-businal IE مثلي الأولي، مما يدل على أداء قوي في هذا الإعداد لاستخراج الأحداث، والتعرف على الكيان المسمى، ووضع علامات جزء من الكلام، وتحليل التبعية. ثم قم بتطبيق إسقاط البيانات والتدريب الذاتي على ثلاثة مهام عبر ثمانية لغات مستهدفة. نظرا لعدم وجود مجموعة واحدة من التقنيات الأفضل عبر جميع المهام، فإننا نشجع الممارسين على استكشاف تكوينات مختلفة للتقنيات الموضحة في هذا العمل عند السعي لتحسين التدريب على الصفر.
Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English. While the advance of pretrained multilingual encoders suggests an easy optimism of train on English, run on any language'', we find through a thorough exploration and extension of techniques that a combination of approaches, both new and old, leads to better performance than any one cross-lingual strategy in particular. We explore techniques including data projection and self-training, and how different pretrained encoders impact them. We use English-to-Arabic IE as our initial example, demonstrating strong performance in this setting for event extraction, named entity recognition, part-of-speech tagging, and dependency parsing. We then apply data projection and self-training to three tasks across eight target languages. Because no single set of techniques performs the best across all tasks, we encourage practitioners to explore various configurations of the techniques described in this work when seeking to improve on zero-shot training.
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