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For years the model performance in machine learning obeyed a power-lawrelationship with the model size. For the consideration of parameterefficiency, recent studies focus on increasing model depth rather than width toachieve better performance. In this paper, we study how model width affects theTransformer model through a parameter-efficient multi-path structure. To betterfuse features extracted from different paths, we add three additionaloperations to each sublayer: a normalization at the end of each path, a cheapoperation to produce more features, and a learnable weighted mechanism to fuseall features flexibly. Extensive experiments on 12 WMT machine translationtasks show that, with the same number of parameters, the shallower multi-pathmodel can achieve similar or even better performance than the deeper model. Itreveals that we should pay more attention to the multi-path structure, andthere should be a balance between the model depth and width to train a betterlarge-scale Transformer.

Multi-Path Transformer is Better: A Case Study on Neural Machine Translation