Transformer networks have lead to important progress in language modeling and machine translation. These models include two consecutive modules, a feedforward layer and a self-attention layer. The latter allows the network to capture long term dependencies and are often regarded as the key ingredient in the success of Transformers. Building upon this intuition, we propose a new model that solely consists of attention layers. More precisely, we augment the self-attention layers with persistent memory vectors that play a similar role as the feed-forward layer. Thanks to these vectors, we can remove the feed-forward layer without degrading the performance of a transformer. Our evaluation shows the benefits brought by our model on standard character and word level language modeling benchmarks.
Another recent work REF removed the feed-forward layer in the Transformer block and augmented the self attention layers with a form of persistent memory whose purpose is similar to the feed-forward layer.
Augmenting Self-attention with Persistent Memory
ArXiv
ArXiv
Mathematics Computer Science