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Register a new userNot All Linearizations Are Equally Data-Hungry in Sequence Labeling Parsing
ليست كل الخطوط الإخطارية هي بالتساوي البيانات الجائعة في تحليل وضع التسلسل
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تم اقتراح تغييرات مختلفة لإلقاء تحليل التبعية كوسيلة تسلسل وحل المهمة على النحو التالي: (1) مشكلة اختيار الرأس، (II) العثور على تمثيل للأقواس الرمز المميز كسلاسل قوس، أو (3) ربط تسلسل انتقال جزئي من أالمحلل المحلل القائم على الانتقال إلى الكلمات.ومع ذلك، لا يوجد تفاهم ضئيل حول كيفية التصرف هذه الخطية في إعدادات الموارد المنخفضة.هنا، ندرس أولا كفاءة البيانات الخاصة بهم، محاكاة الإعدادات المقيدة بالبيانات من مجموعة متنوعة من Treebanks Result Resource.ثانيا، نختبر ما إذا كانت هذه الاختلافات تظهر في إعدادات الموارد المنخفضة حقا.تظهر النتائج أن ترميزات اختيار الرأس أكثر كفاءة في البيانات وأداء أفضل في إطار مثالي (ذهب)، ولكن هذه الميزة تختفي إلى حد كبير لصالح التنسيقات القوسين عندما يشبه الإعداد قيد التشغيل تكوين الموارد المنخفضة في العالم الحقيقي.
Different linearizations have been proposed to cast dependency parsing as sequence labeling and solve the task as: (i) a head selection problem, (ii) finding a representation of the token arcs as bracket strings, or (iii) associating partial transition sequences of a transition-based parser to words. Yet, there is little understanding about how these linearizations behave in low-resource setups. Here, we first study their data efficiency, simulating data-restricted setups from a diverse set of rich-resource treebanks. Second, we test whether such differences manifest in truly low-resource setups. The results show that head selection encodings are more data-efficient and perform better in an ideal (gold) framework, but that such advantage greatly vanishes in favour of bracketing formats when the running setup resembles a real-world low-resource configuration.
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