Machine translation (MT) encompasses a variety of methodologies aimed at enhancing the accuracy of translations. In contrast, the process of human-generated translation relies on a wide range of translation techniques, which are crucial for ensuring linguistic adequacy and fluency. This study suggests that these translation techniques could further optimize machine translation if they are automatically identified before being applied to guide the translation process effectively. The study differentiates between two scenarios of the translation process: from-scratch translation and post-editing. For each scenario, a specific set of experiments has been designed to forecast the most appropriate translation techniques. The findings indicate that the predictive accuracy for from-scratch translation reaches 82%, while the post-editing process exhibits even greater potential, achieving an accuracy rate of 93%.

翻译：机器翻译（MT）包含多种旨在提升翻译准确性的方法论。相比之下，人工翻译过程依赖于多种翻译技巧，这些技巧对于确保语言充分性和流畅性至关重要。本研究表明，若能在应用前自动识别这些翻译技巧以有效指导翻译过程，它们或将进一步优化机器翻译。本研究区分了翻译过程的两种场景：从头翻译和译后编辑。针对每种场景，我们设计了一系列实验来预测最合适的翻译技巧。实验结果表明，从头翻译的预测准确率达到82%，而译后编辑过程展现出更优潜力，准确率可达93%。