  In this paper, we study the use of deep Transformer translation model for the
CCMT 2022 Chinese-Thai low-resource machine translation task. We first explore
the experiment settings (including the number of BPE merge operations, dropout
probability, embedding size, etc.) for the low-resource scenario with the
6-layer Transformer. Considering that increasing the number of layers also
increases the regularization on new model parameters (dropout modules are also
introduced when using more layers), we adopt the highest performance setting
but increase the depth of the Transformer to 24 layers to obtain improved
translation quality. Our work obtains the SOTA performance in the
Chinese-to-Thai translation in the constrained evaluation.
