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Register a new userThe DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task
محلل التبعية المحسنة DCU-EPFL في مهمة مشتركة IWPT 2021
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نحن تصف تقديم DCU-EPFL إلى مهمة مشتركة IWPT 2021: من النص الخام لتعزيز التبعيات العالمية. تتضمن المهمة تحليل الرسوم البيانية UD المحسنة، والتي تعد امتدادا لأشجار التبعية الأساسية المصممة لتكون أكثر تسهيلا نحو تمثيل الهيكل الدلالي. يتم التقييم على 29 شجرة Treebanks في 17 لغة ومطلوبة للمشاركين لتحليل البيانات من كل لغة تبدأ من السلاسل الخام. يستخدم نهجنا خط أنابيب Stanza لمعالجة الملفات النصية، XLM-Roberta للحصول على تمثيلات رمزية في السياق، ونموذج تسجيل الحافة والعلامات للتنبؤ الرسم البياني المحسن. أخيرا، ندير نص PostProcessing لضمان جميع مخرجاتنا هي الرسوم البيانية UD المحسنة سارية المفعول. يضع نظامنا السادس من أصل 9 مشاركا مع درجة مرفق محسنة خشنة (ELAS) 83.57. نقوم بإجراء تجارب إضافية بعد الموعد النهائي والتي تشمل استخدام Trankit لمعالجة ما قبل المعالجة، XLM-Roberta Large Protectenation، وتعلم المتعدد التعلم بين محلل التبعية الأساسية والمعززة. جميع هذه التعديلات تحسن النتيجة الأولية ونظامنا النهائي لديه إيلاس خشن 88.04.
We describe the DCU-EPFL submission to the IWPT 2021 Parsing Shared Task: From Raw Text to Enhanced Universal Dependencies. The task involves parsing Enhanced UD graphs, which are an extension of the basic dependency trees designed to be more facilitative towards representing semantic structure. Evaluation is carried out on 29 treebanks in 17 languages and participants are required to parse the data from each language starting from raw strings. Our approach uses the Stanza pipeline to preprocess the text files, XLM-RoBERTa to obtain contextualized token representations, and an edge-scoring and labeling model to predict the enhanced graph. Finally, we run a postprocessing script to ensure all of our outputs are valid Enhanced UD graphs. Our system places 6th out of 9 participants with a coarse Enhanced Labeled Attachment Score (ELAS) of 83.57. We carry out additional post-deadline experiments which include using Trankit for pre-processing, XLM-RoBERTa LARGE, treebank concatenation, and multitask learning between a basic and an enhanced dependency parser. All of these modifications improve our initial score and our final system has a coarse ELAS of 88.04.
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