  Although Transformers with fully connected self-attentions are powerful to
model long-term dependencies, they are struggling to scale to long texts with
thousands of words in language modeling. One of the solutions is to equip the
model with a recurrence memory. However, existing approaches directly reuse
hidden states from the previous segment that encodes contexts in a
uni-directional way. As a result, this prohibits the memory to dynamically
interact with the current context that provides up-to-date information for
token prediction. To remedy this issue, we propose Look-Ahead Memory (LaMemo)
that enhances the recurrence memory by incrementally attending to the
right-side tokens, and interpolating with the old memory states to maintain
long-term information in the history. LaMemo embraces bi-directional attention
and segment recurrence with an additional computation overhead only linearly
proportional to the memory length. Experiments on widely used language modeling
benchmarks demonstrate its superiority over the baselines equipped with
different types of memory.
