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ملثمين الحقول العشوائية المشروطة للحصول على تسلسل
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النماذج العصبية العشوائية الشرطية (CRF) النماذج العصبية القائمة هي من بين أكثر طرق أداء لحل مشاكل وضع التسلسل.على الرغم من نجاحها الكبير، إلا أن CRF لديه القصور في توليد تسلسلات غير قانونية في بعض الأحيان، على سبيل المثالتسلسلات تحتوي على علامة I- '' مباشرة بعد علامة o ''، ممنوع من مخطط الوسم الحيوي الأساسي.في هذا العمل، نقترح حقل عشوائي مشروط ملثم (MCRF)، وسهل تنفيذ البديل CRF الذي يفرض قيودا على مسارات المرشحين خلال كل من مراحل التدريب وفك الشفرة.نظرا لأن الطريقة المقترحة يحل تماما هذه المشكلة وتجلب تحسنا كبيرا على النماذج القائمة على CRF الموجودة مع تكلفة إضافية بالقرب من الصفر.
Conditional Random Field (CRF) based neural models are among the most performant methods for solving sequence labeling problems. Despite its great success, CRF has the shortcoming of occasionally generating illegal sequences of tags, e.g. sequences containing an I-'' tag immediately after an O'' tag, which is forbidden by the underlying BIO tagging scheme. In this work, we propose Masked Conditional Random Field (MCRF), an easy to implement variant of CRF that impose restrictions on candidate paths during both training and decoding phases. We show that the proposed method thoroughly resolves this issue and brings significant improvement over existing CRF-based models with near zero additional cost.
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