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Spellbert: نموذج محدود يدويا خفيف الوزن لفحص الإملاء الصيني
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تحقق الإملاء الصيني (CSC) هو اكتشاف أخطاء الإملاء الصيني وتصحيحها.تستخدم العديد من النماذج من الارتباك المحدد مسبقا لتعلم رسم خرائط بين الأحرف الصحيحة وأهمياتها المماثلة المماثلة البصرية أو مماثلة ولكن قد يكون الرسم الخرائط خارج المجال.تحقيقا لهذه الغاية، نقترح Spellbert، وهو نموذج مسبق مع ميزات إضافية قائمة على الرسم البياني ومستقلة على مجموعة الارتباك.لالتقاط الأنماطين الخاطئة بشكل صريح، فإننا نوظف شبكة عصبية رسم بيانية لإدخال معلومات جذرية وبرينيا كميزات بصرية وعصرية.لتحسين الصدر هذه الميزات مع تمثيلات الأحرف، ونحن نضع نموذج لغة ملثم على حد سواء مهام التدريب مسبقا.من خلال هذا التدريب المسبق الأغنياء المظلات، يمكن أن تظهر Spellbert مع نصف حجم Bert فقط أداء تنافسي وجعل أحدث نتيجة في مجموعة بيانات OCR حيث لا يتم تغطية معظم الأخطاء من قبل مجموعة الارتباك الموجودة.
Chinese Spelling Check (CSC) is to detect and correct Chinese spelling errors. Many models utilize a predefined confusion set to learn a mapping between correct characters and its visually similar or phonetically similar misuses but the mapping may be out-of-domain. To that end, we propose SpellBERT, a pretrained model with graph-based extra features and independent on confusion set. To explicitly capture the two erroneous patterns, we employ a graph neural network to introduce radical and pinyin information as visual and phonetic features. For better fusing these features with character representations, we devise masked language model alike pre-training tasks. With this feature-rich pre-training, SpellBERT with only half size of BERT can show competitive performance and make a state-of-the-art result on the OCR dataset where most of the errors are not covered by the existing confusion set.
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