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In this paper, we propose Dynamic Compressive Transformer (DCT), atransformer-based framework for modeling the unbounded sequence. In contrast tothe previous baselines which append every sentence representation to memory,conditionally selecting and appending them is a more reasonable solution todeal with unlimited long sequences. Our model uses a policy that determineswhether the sequence should be kept in memory with a compressed state ordiscarded during the training process. With the benefits of retainingsemantically meaningful sentence information in the memory system, ourexperiment results on Enwik8 benchmark show that DCT outperforms the previousstate-of-the-art (SOTA) model.

DCT: Dynamic Compressive Transformer for Modeling Unbounded Sequence