  Although dominant in natural language processing, transformer-based models
remain challenged by the task of long-sequence processing, because the
computational cost of self-attention operations in transformers swells
quadratically with the input sequence length. To alleviate the complexity of
long-sequence processing, we propose a simple framework to enable the
offthe-shelf pre-trained transformers to process much longer sequences, while
the computation and memory costs remain growing linearly with the input
sequence lengths. More specifically, our method divides each long-sequence
input into a batch of chunks, then aligns the interchunk information during the
encoding steps, and finally selects the most representative hidden states from
the encoder for the decoding process. To extract inter-chunk semantic
information, we align the start and end token embeddings among chunks in each
encoding transformer block. To learn an effective hidden selection policy, we
design a dual updating scheme inspired by reinforcement learning, which regards
the decoders of transformers as environments, and the downstream performance
metrics as the rewards to evaluate the hidden selection actions. Our empirical
results on real-world long-text summarization and reading comprehension tasks
demonstrate effective improvements compared to prior longsequence processing
baselines.
