Bidirectional long-range parser for sequential data understanding

The transformer is a powerful data-modeling framework responsible for remarkable performance on a wide range of tasks. However, transformers are limited in terms of scalability as it is suboptimal and inefficient to process long-sequence data. To this purpose we introduce BLRP (Bidirectional Long-Range Parser), a novel and versatile attention mechanism designed to increase performance and efficiency on long-sequence tasks. It leverages short- and long-range heuristics in the form of a local sliding-window approach combined with a global bidirectional latent-space synthesis technique. We show the benefits and versatility of our approach on vision and language domains by demonstrating competitive results against state-of-the-art methods on the Long-Range-Arena and CIFAR benchmarks together with ablations demonstrating the computational efficiency.