Implicit Memory Transformer for Computationally Efficient Simultaneous
  Speech Translation
Matthew Raffel Lizhong Chen
Simultaneous speech translation is an essential communication task difficult
for humans whereby a translation is generated concurrently with oncoming speech
inputs. For such a streaming task, transformers using block processing to break
an input sequence into segments have achieved state-of-the-art performance at a
reduced cost. Current methods to allow information to propagate across
segments, including left context and memory banks, have faltered as they are
both insufficient representations and unnecessarily expensive to compute. In
this paper, we propose an Implicit Memory Transformer that implicitly retains
memory through a new left context method, removing the need to explicitly
represent memory with memory banks. We generate the left context from the
attention output of the previous segment and include it in the keys and values
of the current segment's attention calculation. Experiments on the MuST-C
dataset show that the Implicit Memory Transformer provides a substantial
speedup on the encoder forward pass with nearly identical translation quality
when compared with the state-of-the-art approach that employs both left context
and memory banks.