In recent years, multilingual machine translation models have achieved promising performance on low-resource language pairs by sharing information between similar languages, thus enabling zero-shot translation. To overcome the "curse of multilinguality", these models often opt for scaling up the number of parameters, which makes their use in resource-constrained environments challenging. We introduce SMaLL-100, a distilled version of the M2M-100 (12B) model, a massively multilingual machine translation model covering 100 languages. We train SMaLL-100 with uniform sampling across all language pairs and therefore focus on preserving the performance of low-resource languages. We evaluate SMaLL-100 on different low-resource benchmarks: FLORES-101, Tatoeba, and TICO-19 and demonstrate that it outperforms previous massively multilingual models of comparable sizes (200-600M) while improving inference latency and memory usage. Additionally, our model achieves comparable results to M2M-100 (1.2B), while being 3.6x smaller and 4.3x faster at inference. Code and pre-trained models: https://github.com/alirezamshi/small100
翻译:近年来,多语种机器翻译模式通过在类似语言之间共享信息,在低资源语言配对上取得了有希望的成绩,从而实现了零点翻译。为了克服“多语种的诅咒 ”, 这些模式往往选择扩大参数数量,使其在资源紧缺的环境中使用具有挑战性。我们引入了SMaL-100(12B),这是M2M-100(12B)模式的蒸馏版,这是一个涵盖100种语言的大规模多语言机器翻译模式。我们培训SMaL-100,对所有语言配对进行统一取样,因此侧重于保护低资源语言的性能。我们评估了SMaLL-100, 其依据是不同的低资源基准:FLORES-101、Tatoeba和TINO-19, 并表明它比以前规模相当的大规模多语言模式(200-600M)更符合预测力和记忆用量。此外,我们的模型取得了与M2M-100(1.2B)相近的结果,同时在推论处更小3.6x和4.3x较快。代码和受训练前模型: http://gis://giththhubub.com/alirezaslamam/s 100/stanm。