اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدQuery-Key Normalization for Transformers
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نشر من قبل
Prudhvi Raj Dachapally
تاريخ النشر
2020
مجال البحث
الهندسة المعلوماتية
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English
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Low-resource language translation is a challenging but socially valuable NLP task. Building on recent work adapting the Transformers normalization to this setting, we propose QKNorm, a normalization technique that modifies the attention mechanism to make the softmax function less prone to arbitrary saturation without sacrificing expressivity. Specifically, we apply $ell_2$ normalization along the head dimension of each query and key matrix prior to multiplying them and then scale up by a learnable parameter instead of dividing by the square root of the embedding dimension. We show improvements averaging 0.928 BLEU over state-of-the-art bilingual benchmarks for 5 low-resource translation pairs from the TED Talks corpus and IWSLT15.
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