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Register a new userDefinition Modelling for Appropriate Specificity
تحديد النمذجة للحصول على الخصوصية المناسبة
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تهدف تقنيات توليد الجيل إلى توليد تعريف كلمة أو عبارة مستهدفة بالنظر إلى السياق.في الدراسات السابقة، واجه الباحثون قضايا مختلفة مثل مشكلة خارج المفردات ومشاكل أكثر من اللازم.التعاريف المفرطة المحددة الحالية معاني الكلمة الضيقة، في حين أن التعاريف الخاضعة لها المعاني العامة والسياق غير حساسة.هنا، نقترح طريقة لتوليد التعريف مع الخصوصية المناسبة.تعالج الطريقة المقترحة المشكلات المذكورة أعلاه من خلال الاستفادة من نموذج ترميز التشفير المدرب مسبقا، وهي محول نقل النص إلى النص، وإدخال آلية إعادة الترتيب لنموذج النوعية في التعاريف.تشير النتائج التجريبية إلى مجموعات بيانات التقييم القياسية إلى أن طريقتنا تتفوق بشكل كبير على الطريقة السابقة للحالة السابقة.علاوة على ذلك، يؤكد التقييم اليدوي أن أسلوبنا يعالج بشكل فعال مشاكل أكثر من / أقل خصوصية.
Definition generation techniques aim to generate a definition of a target word or phrase given a context. In previous studies, researchers have faced various issues such as the out-of-vocabulary problem and over/under-specificity problems. Over-specific definitions present narrow word meanings, whereas under-specific definitions present general and context-insensitive meanings. Herein, we propose a method for definition generation with appropriate specificity. The proposed method addresses the aforementioned problems by leveraging a pre-trained encoder-decoder model, namely Text-to-Text Transfer Transformer, and introducing a re-ranking mechanism to model specificity in definitions. Experimental results on standard evaluation datasets indicate that our method significantly outperforms the previous state-of-the-art method. Moreover, manual evaluation confirms that our method effectively addresses the over/under-specificity problems.
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Loading models pre-trained on the large-scale corpus in the general domain and fine-tuning them on specific downstream tasks is gradually becoming a paradigm in Natural Language Processing. Previous investigations prove that introducing a further pre
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