Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation

• URL: http://arxiv.org/abs/2212.10551v3
• Date: Wed, 19 Jul 2023 05:52:32 GMT
• Title: Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation
• Authors: Fei Yuan, Yinquan Lu, WenHao Zhu, Lingpeng Kong, Lei Li, Yu Qiao, Jingjing Xu
• Abstract summary: We propose a novel efficient training recipe, upon which we build an effective detachable model, Lego-MT. Experiments show that Lego-MT with 1.2B parameters brings an average gain of 3.2 spBLEU. The proposed training recipe brings a 28.2$times$ speedup over the conventional multi-way training method.
• Score: 48.37939354609931
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multilingual neural machine translation (MNMT) aims to build a unified model for many language directions. Existing monolithic models for MNMT encounter two challenges: parameter interference among languages and inefficient inference for large models. In this paper, we revisit the classic multi-way structures and develop a detachable model by assigning each language (or group of languages) to an individual branch that supports plug-and-play training and inference. To address the needs of learning representations for all languages in a unified space, we propose a novel efficient training recipe, upon which we build an effective detachable model, Lego-MT. For a fair comparison, we collect data from OPUS and build a translation benchmark covering 433 languages and 1.3B parallel data. Experiments show that Lego-MT with 1.2B parameters brings an average gain of 3.2 spBLEU. It even outperforms M2M-100 with 12B parameters. The proposed training recipe brings a 28.2$\times$ speedup over the conventional multi-way training method.\footnote{ \url{https://github.com/CONE-MT/Lego-MT}.}

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