  Triangular machine translation is a special case of low-resource machine
translation where the language pair of interest has limited parallel data, but
both languages have abundant parallel data with a pivot language. Naturally,
the key to triangular machine translation is the successful exploitation of
such auxiliary data. In this work, we propose a transfer-learning-based
approach that utilizes all types of auxiliary data. As we train auxiliary
source-pivot and pivot-target translation models, we initialize some parameters
of the pivot side with a pre-trained language model and freeze them to
encourage both translation models to work in the same pivot language space, so
that they can be smoothly transferred to the source-target translation model.
Experiments show that our approach can outperform previous ones.
