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arXiv · 2203.09027

Triangular Transfer: Freezing the Pivot for Triangular Machine Translation

Meng Zhang·Liangyou Li·Qun Liu
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Abstract

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.

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Meng Zhang, Liangyou Li, Qun Liu. 2022-03-17. Triangular Transfer: Freezing the Pivot for Triangular Machine Translation. https://arxiv.org/abs/2203.09027

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