Speeding Up Entmax

Abstract

Softmax is the de facto standard in modern neural networks for language processing when it comes to normalizing logits. However, by producing a dense probability distribution each token in the vocabulary has a nonzero chance of being selected at each generation step, leading to a variety of reported problems in text generation. α-entmax of Peters et al. ( 2019) solves this problem, but is considerably slower than softmax.In this paper, we propose an alternative to αentmax, which keeps its virtuous characteristics, but is as fast as optimized softmax and achieves on par or better performance in machine translation task.