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Register a new userOvercoming Poor Word Embeddings with Word Definitions
التغلب على Adveddings كلمة سيئة مع تعريفات الكلمات
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تعتمد نماذج فهم اللغة الطبيعية الحديثة على أشرطة الكلمات الفرعية مسبقا، ولكن قد تحتاج التطبيقات إلى سبب الكلمات التي لم تكن أبدا أو نادرا ما ينظر إليها أثناء الاحتجاج.نظير على أن الأمثلة التي تعتمد بشكل نقدي على كلمة ندرة هي أكثر تحديا لنماذج الاستدلال اللغوية الطبيعية.ثم نستكشف كيف يمكن أن يتعلم نموذج التعريفات، المقدمة في النص الطبيعي، للتغلب على هذا الإعاقة.عادة ما يكون فهم النموذج الخاص بتعريف ضعيفا أكثر أضعف من تضمين كلمة مصممة بشكل جيد، لكنه يستعيد معظم فجوة الأداء من استخدام كلمة غير مدربة تماما.
Modern natural language understanding models depend on pretrained subword embeddings, but applications may need to reason about words that were never or rarely seen during pretraining. We show that examples that depend critically on a rarer word are more challenging for natural language inference models. Then we explore how a model could learn to use definitions, provided in natural text, to overcome this handicap. Our model's understanding of a definition is usually weaker than a well-modeled word embedding, but it recovers most of the performance gap from using a completely untrained word.
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الغاية من هذا البحث بناء نظام لتصنيف نطق الأرقام الانكليزية وذلك بالاعتماد على نماذج ماركوف المخفية في التصنيف وذلك بالاعتماد على طيف الإشارة في استخراج سمات الإشارات
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