  Efficient transformer variants with linear time complexity have been
developed to mitigate the quadratic computational overhead of the vanilla
transformer. Among them are low-rank projection methods such as Linformer and
kernel-based Transformers. Despite their unique merits, they usually suffer
from a performance drop comparing with the vanilla transformer on many sequence
generation tasks, and often fail to obtain computation gain when the generation
is short. We propose MemSizer, an approach towards closing the performance gap
while improving the efficiency even with short generation. It projects the
source sequences into lower dimension representations like Linformer, while
enjoying efficient recurrent-style incremental computation similar to
kernel-based transformers. This yields linear computation time and constant
memory complexity at inference time. MemSizer also employs a lightweight
multi-head mechanism which renders the computation as light as a single-head
model. We demonstrate that MemSizer provides an improved balance between
efficiency and accuracy over the vanilla transformer and other efficient
transformer variants in three typical sequence generation tasks, including
machine translation, abstractive text summarization, and language modeling.
