Papers › Query-Key Normalization for Transformers

Query-Key Normalization for Transformers

8 Oct 2020Findings of the Association for Computational Linguistics 2020arXiv:2010.04245archive 2025-07-28

Alex Henry, Prudhvi Raj Dachapally, Shubham Pawar, Yuxuan Chen

Low-resource language translation is a challenging but socially valuable NLP task. Building on recent work adapting the Transformer's 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 ℓ₂ 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 IWSLT'15.

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