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Register a new userRobertNLP at the IWPT 2021 Shared Task: Simple Enhanced UD Parsing for 17 Languages
Robertnlp في المهمة المشتركة Iwpt 2021: تحليل UD بسيط محسن لمدة 17 لغة
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تقدم هذه الورقة نظام تحليل التبعية متعدد اللغات كما هو مستخدم في المهمة المشتركة IWPT 2021 على التحليل في التبعيات العالمية المعززة.يتكون نظامنا من مصنف BiAffine غير المعسم الذي يعمل مباشرة على مدينتي XLM-R التي تم ضبطها بشكل مباشر ويولد الرسوم البيانية UD المحسنة من خلال التنبؤ بأفضل تسمية التبعية (أو عدم وجود اعتماد) لكل زوج من الرموز.لتجنب قضايا Sparsity الناتجة عن ملصقات التبعية المعجمية، نحل محل البنود المعجمية في العلاقات مع العناصر النائبة في وقت التدريب ووقت التنبؤ، لاحقا لاحقا من التحليل عبر نظام التعلم القائم على القاعدة / الجهاز الهجينة.بالإضافة إلى ذلك، نحن نستخدم نموذج النموذج في وقت التنبؤ.يحقق نظامنا دقة تحليل عالية على بيانات الاختبار العمياء، المرتبة الثالثة من أصل 9 مع متوسط درجة ELAS F1 من 86.97.
This paper presents our multilingual dependency parsing system as used in the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies. Our system consists of an unfactorized biaffine classifier that operates directly on fine-tuned XLM-R embeddings and generates enhanced UD graphs by predicting the best dependency label (or absence of a dependency) for each pair of tokens. To avoid sparsity issues resulting from lexicalized dependency labels, we replace lexical items in relations with placeholders at training and prediction time, later retrieving them from the parse via a hybrid rule-based/machine-learning system. In addition, we utilize model ensembling at prediction time. Our system achieves high parsing accuracy on the blind test data, ranking 3rd out of 9 with an average ELAS F1 score of 86.97.
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We describe the second IWPT task on end-to-end parsing from raw text to Enhanced Universal Dependencies. We provide details about the evaluation metrics and the datasets used for training and evaluation. We compare the approaches taken by participating teams and discuss the results of the shared task, also in comparison with the first edition of this task.
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