Translation has played a crucial role in improving the performance on
multilingual tasks: (1) to generate the target language data from the source
language data for training and (2) to generate the source language data from
the target language data for inference. However, prior works have not
considered the use of both translations simultaneously. This paper shows that
combining them can synergize the results on various multilingual sentence
classification tasks. We empirically find that translation artifacts stylized
by translators are the main factor of the performance gain. Based on this
analysis, we adopt two training methods, SupCon and MixUp, considering
translation artifacts. Furthermore, we propose a cross-lingual fine-tuning
algorithm called MUSC, which uses SupCon and MixUp jointly and improves the
performance. Our code is available at this https URL.

本研究探讨在多语言任务中同时使用源语言和目标语言翻译数据的效果，并得出使用翻译工具造成的风格化翻译差异是提高性能的主要因素。此外，我们提出了一种名为 MUSC 的跨语言微调算法，该方法结合了 SupCon 和 MixUp，可以进一步提高多语言分类任务的性能。