arXiv:2111.06832 공식 코드 · Papers with Code

Speeding Up Entmax

Maxat Tezekbayev, Vassilina Nikoulina, Matthias Gallé, Zhenisbek Assylbekov

제출 2021-11-12

원문 초록

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. $\alpha$-entmax of Peters et al. (2019, arXiv:1905.05702) solves this problem, but is considerably slower than softmax. In this paper, we propose an alternative to $\alpha$-entmax, which keeps its virtuous characteristics, but is as fast as optimized softmax and achieves on par or better performance in machine translation task.

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