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Register a new userHierarchical Character Tagger for Short Text Spelling Error Correction
الطابع الهرمي Tagger لتصحيح الأخطاء الإملائي النصي
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تتضمن النهج الحديثة لإملاء مشكلة تصحيح الأخطاء الإملائي نماذج SEQ2SEQ القائمة على المحولات، والتي تتطلب مجموعات تدريبية كبيرة وتعاني من وقت الاستدلال البطيء؛وتسلسل نماذج وضع التسلسل المستندة إلى ترميز المحولات مثل بيرت، والتي تنطوي على مساحة تسمية المستوى المميز وبالتالي قاموس مفردات محددة مسبقا مسبقا.في هذه الورقة، نقدم طراز Tagger Tagner التسلسل الهرمي، أو HCTAGGER، لتصحيح الأخطاء الإملائي في النص القصير.نستخدم نموذج لغة مدرب مسبقا على مستوى الحرف كتشفير نصي، ثم يتوقع تحرير مستوى الأحرف لتحويل النص الأصلي إلى شكل خالي من الأخطاء مع مساحة تسمية أصغر بكثير.للحصول على فك التشفير، نقترح نهجا هرميا متعدد المهام للتخفيف من مسألة توزيع الملصقات الطويلة الذيل دون تقديم معلمات نموذجية إضافية.تثبت التجارب في مجموعات بيانات تصحيح أخطاء الأخشاب العامة أن HCTAGGER هو نهج دقيق وأسرع بكثير من العديد من النماذج الموجودة.
State-of-the-art approaches to spelling error correction problem include Transformer-based Seq2Seq models, which require large training sets and suffer from slow inference time; and sequence labeling models based on Transformer encoders like BERT, which involve token-level label space and therefore a large pre-defined vocabulary dictionary. In this paper we present a Hierarchical Character Tagger model, or HCTagger, for short text spelling error correction. We use a pre-trained language model at the character level as a text encoder, and then predict character-level edits to transform the original text into its error-free form with a much smaller label space. For decoding, we propose a hierarchical multi-task approach to alleviate the issue of long-tail label distribution without introducing extra model parameters. Experiments on two public misspelling correction datasets demonstrate that HCTagger is an accurate and much faster approach than many existing models.
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