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Register a new userAn Alignment-Agnostic Model for Chinese Text Error Correction
نموذج محاذاة غير مرغقة لتصحيح خطأ النص الصيني
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تحقق هذه الورقة في كيفية تصحيح أخطاء النص الصينية مع أنواع من الأحرف الخاطئة والمفقودة والمتغمة، وهي شائعة للمتحدثين الأصليين الصينيين.يمكن لمعظم النماذج الموجودة على الإطار الصحيح على الكشف عن تصحيح الأحرف الخاطئة، ولكن لا يمكن التعامل مع الأحرف المفقودة أو الزائدة بسبب التناقض بين المدخلات والمخرجات النموذجية.على الرغم من أن أساليب العلامات المستندة إلى SEQ2SEQ أو التسلسل تقدم حلولا لأنواع الخطأ الثلاثة وحققت نتائج جيدة نسبيا في سياق اللغة الإنجليزية، فإنها لا تؤدي بشكل جيد في السياق الصيني وفقا تجاربنا.في عملنا، نقترح إطارا جديدا للكشف عن المحاذاة على المكتشفة التي يمكن أن تعالج كل من المواقف المحاذاة وغير المحاذاة ويمكن أن تكون بمثابة نموذج ابدأ بارد عند عدم توفر بيانات التعليق التوضيحي.تظهر النتائج التجريبية على ثلاث مجموعات بيانات أن طريقتنا فعالة وتحقق أداء أفضل من أحدث النماذج المنشورة.
This paper investigates how to correct Chinese text errors with types of mistaken, missing and redundant characters, which are common for Chinese native speakers. Most existing models based on detect-correct framework can correct mistaken characters, but cannot handle missing or redundant characters due to inconsistency between model inputs and outputs. Although Seq2Seq-based or sequence tagging methods provide solutions to the three error types and achieved relatively good results in English context, they do not perform well in Chinese context according to our experiments. In our work, we propose a novel alignment-agnostic detect-correct framework that can handle both text aligned and non-aligned situations and can serve as a cold start model when no annotation data are provided. Experimental results on three datasets demonstrate that our method is effective and achieves a better performance than most recent published models.
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