Developing a unified multilingual model has long been a pursuit for machine translation. However, existing approaches suffer from performance degradation -- a single multilingual model is inferior to separately trained bilingual ones on rich-resource languages. We conjecture that such a phenomenon is due to interference caused by joint training with multiple languages. To accommodate the issue, we propose CIAT, an adapted Transformer model with a small parameter overhead for multilingual machine translation. We evaluate CIAT on multiple benchmark datasets, including IWSLT, OPUS-100, and WMT. Experiments show that CIAT consistently outperforms strong multilingual baselines on 64 of total 66 language directions, 42 of which see above 0.5 BLEU improvement. Our code is available at \url{https://github.com/Yaoming95/CIAT}~.
翻译:长期以来,开发统一的多语文模式一直是对机器翻译的一种追求,但是,现有的方法存在性能退化的问题 -- -- 单一的多语文模式在丰富资源语言上比经过单独训练的双语模式低。我们推测,这种现象是由于与多种语言联合培训造成的干扰造成的。为了解决这个问题,我们提议采用经调整的变换模型,即具有小参数的多语种机器翻译管理费的变换模型。我们评估了多基准数据集的CIAT,包括IWSLT、OPUS-100和WMT。 实验显示,CICT在总共66种语言方向的64个方向上,始终超越了强大的多语种基线,其中42个方向比0.5 BLEU改进了0.5以上。我们的代码可在以下查阅:<url{https://github.com/Yaoming95/CIAT>。