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Various natural language processing (NLP) tasks necessitate models that areefficient and small based on their ultimate application at the edge or in otherresource-constrained environments. While prior research has reduced the size ofthese models, increasing computational efficiency without considerableperformance impacts remains difficult, especially for autoregressive tasks.This paper proposes modular linearized attention (MLA), which combines multipleefficient attention mechanisms, including cosFormer, to maximize inferencequality while achieving notable speedups. We validate this approach on severalautoregressive NLP tasks, including speech-to-text neural machine translation(S2T NMT), speech-to-text simultaneous translation (SimulST), andautoregressive text-to-spectrogram, noting efficiency gains on TTS andcompetitive performance for NMT and SimulST during training and inference.

Improving Autoregressive NLP Tasks via Modular Linearized Attention