Multilingual neural machine translation for low-resource languages by twinning important nodes

Abstract

Multilingual neural machine translation (MNMT) is a novel machine translation approach that benefits from large multilingual resources. However, its performance drops significantly when training with low-resource languages due to the reliance on parameter sharing and data size. In this paper, a new method is proposed to improve the performance of MNMT for a pair of languages where the target language is low-resource. The main idea of this study is to find important nodes that have parameters connected to them that negatively affect an MNMT model and then split those nodes into two sub nodes. Then, the model selects the important sub node that has an effect on the specific language pair to create a twin sub node. This twin sub node helps to strengthen the translation quality of the specific language pair without having a negative effect on other languages. The proposed method works in four steps as: 1) training an MNMT model with parameter sharing over multiple languages, 2) selecting important nodes which negatively affect the MNMT, 3) splitting important nodes into sub nodes, and 4) Twining important sub nodes. The proposed method has been evaluated using several multilingual datasets, including TED 2013, TED 2020, and BIBLE, by examining English-Persian language as a case study. The obtained results show that the proposed method yields the best results for one-to-many and many-to-many models according to the average BLEU value and semantic similarity. The results also show that the proposed method has given better results than other well-known large language models, such as ChatGPT, BING GPT4, and the Google Neural Machine Translation (GNMT) model, when applied to a low-resource language. © 2025 Elsevier B.V.