April 19, 2019
Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current time step. In this paper, we show that a very lightweight convolution can perform competitively to the best reported self-attention results. Next, we introduce dynamic convolutions which are simpler and more efficient than self-attention. We predict separate convolution kernels based solely on the current time-step in order to determine the importance of context elements. The number of operations required by this approach scales linearly in the input length, whereas self-attention is quadratic. Experiments on large-scale machine translation, language modeling and abstractive summarization show that dynamic convolutions improve over strong self-attention models. On the WMT'14 English-German test set dynamic convolutions achieve a new state of the art of 29.7 BLEU.
Publisher
ICLR
December 15, 2021
Akash Bharadwaj, Graham Cormode
December 15, 2021
August 30, 2021
Yun Wang, Christian Fuegen, Didi Zhang, Gil Keren, Kaustubh Kalgaonkar, Ju Lin
August 30, 2021
January 09, 2021
Baptiste Rozière, Camille Couprie, Olivier Teytaud, Andry Rasoanaivo, Hanhe Lin, Nathanaël Carraz Rakotonirina, Vlad Hosu
January 09, 2021
January 09, 2021
Jean Tarbouriech, Alessandro Lazaric, Matteo Pirotta, Michal Valko
January 09, 2021