ATTENTION NEURAL NETWORKS WITH LINEAR UNITS
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input to generate a network output. In one aspect, one of the systems includes an attention neural network configured to perform the machine learning task, the attention neural network including one or more attention layers, each attention layer comprising an attention sub-layer and a feed-forward sub-layer that applies an element-wise multiplication between two vectors generated as a result of two different linear transformations performed on the same attended layer input.
1 . A system for performing a machine learning task on a network input to generate a network output, the system comprising one or more computers and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to implement:
an attention neural network configured to perform the machine learning task, the attention neural network comprising a plurality of attention layers, each attention layer comprising an attention sub-layer and a feed-forward sub-layer, the attention sub-layer configured to:
receive an input sequence for the attention layer comprising a respective layer input at each of one or more positions; and
generate an attended input sequence at least in part by applying an attention mechanism to the input sequence for the attention layer, the attended input sequence comprising a respective attended layer input at each of the one or more positions, and the feed-forward sub-layer configured to:
receive the attended input sequence; and
generate an output sequence for the attention layer from the attended input sequence, the output sequence comprising a respective layer output at each of the one or more positions, and the generating comprising, for each of the positions:
generating a first transformed input, comprising applying a first linear transformation to the attended layer input at the position;
generating a second transformed input by applying a second linear transformation to the attended layer input at the position;
generating a third transformed input by performing an element-wise multiplication between the first transformed input and the second transformed input; and
generating the layer output at the position from the third transformed input.