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Register a new userZero-shot cross-lingual Meaning Representation Transfer: Annotation of Hungarian using the Prague Functional Generative Description
صفر - تسديدة تعاوني المعنى المتبادل التحويل: شرح الهنغارية باستخدام الوصف الفني براغ وظيفي
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في هذه الورقة، نقدم نتائج تجاربنا المتعلقة بالأداء الصفر - اللغات اللغات في المحلل الدلالي لحكم Perin إلى الرسم البياني. طبقنا طراز PTG المدربين باستخدام محلل Perin على جريدة 740k-Token Czech إلى الهنغارية. قمنا بتقييم أداء المحلل المحلل باستخدام أداة التقييم الرسمية للمهمة المشتركة MRP 2020. تم إنشاء الشروح الهنغارية القياسية الذهبية عن طريق التصحيح اليدوي لإخراج المحلل في أعقاب دليل الشرح للمستوى Tectogrammatical من TreeBank TreeBank براغ. ومع ذلك، فإن نموذج إنجليزي مدرب على كوربس صحيفة إنجليزية أكبر مليونا متاحا، وجدنا أن النموذج التشيكي أدى بشكل كبير على المدخلات الهنغارية بسبب حقيقة أن الهنغاري يشبه بشكل أكثر تشبه التشيكية من الإنجليزية. لقد وجدنا أن التحويل الصفرية لقطة بمعنى PTG يعنى تمثيل عبر اللغات غير البعيدة النموذجية باستخدام نموذج محلل عصبي يعتمد على نموذج لغة سياسي متعدد اللغات يتبعه تصحيح يدوي من قبل خبراء اللغاتين هو سيناريو قابل للتطبيق.
In this paper, we present the results of our experiments concerning the zero-shot cross-lingual performance of the PERIN sentence-to-graph semantic parser. We applied the PTG model trained using the PERIN parser on a 740k-token Czech newspaper corpus to Hungarian. We evaluated the performance of the parser using the official evaluation tool of the MRP 2020 shared task. The gold standard Hungarian annotation was created by manual correction of the output of the parser following the annotation manual of the tectogrammatical level of the Prague Dependency Treebank. An English model trained on a larger one-million-token English newspaper corpus is also available, however, we found that the Czech model performed significantly better on Hungarian input due to the fact that Hungarian is typologically more similar to Czech than to English. We have found that zero-shot transfer of the PTG meaning representation across typologically not-too-distant languages using a neural parser model based on a multilingual contextual language model followed by a manual correction by linguist experts seems to be a viable scenario.
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