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The transformer neural network architecture uses a form of attention in whichthe dot product of query and key is divided by the square root of the keydimension before applying softmax. This scaling of the dot product is designedto avoid the absolute value of the dot products becoming so large that applyingsoftmax leads to vanishing gradients. In this paper, we propose somealternative scalings, including dividing the dot product instead by the sum ofthe key lengths before applying softmax. We use simulated keys and queries toshow that in many situations this appears to be more effective at avoidingregions where applying softmax leads to vanishing gradients.

Alternatives to the Scaled Dot Product for Attention in the Transformer Neural Network Architecture