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Register a new userSystem Description for the CommonGen task with the POINTER model
وصف النظام لمهمة commongen مع نموذج المؤشر
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في تجربة حالية، كنا نقوم باختبار DataSet Commongen للمهمة الهيكلية إلى النص من GEM Living Benchmark مع نموذج مؤشر القيد القائم.يمثل المؤشر هندسة هجينة، والجمع بين النماذج القائمة على الإدراج والمحول، والتنبؤ بالكمية وموقع الإدراج في نفس الوقت.لذلك يتم إنشاء النص تدريجيا بطريقة غير متوازية غير تلقائية، بالنظر إلى مجموعة الكلمات الرئيسية.كان النموذج المحدد ضبطه بشكل جيد على تقسيم تدريب لمجموعة بيانات Commungen وتم مقارنة نتيجة الجيل بالتحقق من الصحة والتحدي.تتم مناقشة مخرجات المقاييس المستلمة، والتي تقيس المعادلات المعجمية، التشابه الدلالي والتنوع، في التفاصيل في وصف النظام الحالي.
In a current experiment we were testing CommonGen dataset for structure-to-text task from GEM living benchmark with the constraint based POINTER model. POINTER represents a hybrid architecture, combining insertion-based and transformer paradigms, predicting the token and the insertion position at the same time. The text is therefore generated gradually in a parallel non-autoregressive manner, given the set of keywords. The pretrained model was fine-tuned on a training split of the CommonGen dataset and the generation result was compared to the validation and challenge splits. The received metrics outputs, which measure lexical equivalence, semantic similarity and diversity, are discussed in details in a present system description.
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