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Transformer has shown promising results in many sequence to sequencetransformation tasks recently. It utilizes a number of feed-forwardself-attention layers to replace the recurrent neural networks (RNN) inattention-based encoder decoder (AED) architecture. Self-attention layer learnstemporal dependence by incorporating sinusoidal positional embedding of tokensin a sequence for parallel computing. Quicker iteration speed in training thansequential operation of RNN can be obtained. Deeper layers of the transformeralso make it perform better than RNN-based AED. However, this parallelizationability is lost when applying scheduled sampling training. Self-attention withsinusoidal positional embedding may cause performance degradations for longersequences that have similar acoustic or semantic information at differentpositions as well. To address these problems, we propose to use parallelscheduled sampling (PSS) and relative positional embedding (RPE) to help thetransformer generalize to unseen data. Our proposed methods achieve a 7%relative improvement for short utterances and a 70% relative gain for longutterances on a 10,000-hour Mandarin ASR task.

Improving Generalization of Transformer for Speech Recognition with Parallel Schedule Sampling and Relative Positional Embedding