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.
@article{arxiv.2203.09027,
title = {Triangular Transfer: Freezing the Pivot for Triangular Machine Translation},
author = {Meng Zhang and Liangyou Li and Qun Liu},
journal= {arXiv preprint arXiv:2203.09027},
year = {2022}
}