Translating major language resources to build minor language resources becomes a widely-used approach. Particularly in translating complex data points composed of multiple components, it is common to translate each component separately. However, we argue that this practice often overlooks the interrelation between components within the same data point. To address this limitation, we propose a novel MT pipeline that considers the intra-data relation in implementing MT for training data. In our MT pipeline, all the components in a data point are concatenated to form a single translation sequence and subsequently reconstructed to the data components after translation. We introduce a Catalyst Statement (CS) to enhance the intra-data relation, and Indicator Token (IT) to assist the decomposition of a translated sequence into its respective data components. Through our approach, we have achieved a considerable improvement in translation quality itself, along with its effectiveness as training data. Compared with the conventional approach that translates each data component separately, our method yields better training data that enhances the performance of the trained model by 2.690 points for the web page ranking (WPR) task, and 0.845 for the question generation (QG) task in the XGLUE benchmark.

翻译：将主要语言资源翻译以构建次要语言资源已成为一种广泛采用的方法。特别是在翻译由多个组件组成的复杂数据点时，通常的做法是单独翻译每个组件。然而，我们认为这种做法常常忽略了同一数据点内不同组件之间的相互关系。针对这一局限性，我们提出了一种新颖的机器翻译管线，该管线在实现训练数据的机器翻译时考虑了组件内部的关系。在我们的翻译管线中，数据点中的所有组件被拼接成一个单一的翻译序列，并在翻译后重构为数据组件。我们引入催化剂语句（CS）以增强组件内部关系，并引入指示性标记（IT）以协助将翻译后的序列分解为各自的数据组件。通过我们的方法，我们不仅在翻译质量本身取得了显著提升，还提高了其作为训练数据的有效性。与单独翻译每个数据组件的传统方法相比，我们的方法生成了更优的训练数据，使得在XGLUE基准测试中，网页排序（WPR）任务上训练模型的性能提升了2.690个百分点，问题生成（QG）任务上提升了0.845个百分点。