Nowadays, in the globalised context in which we find ourselves, language barriers can still be an obstacle to accessing information. On occasions, it is impossible to satisfy the demand for translation by relying only in human translators, therefore, tools such as Machine Translation (MT) are gaining popularity due to their potential to overcome this problem. Consequently, research in this field is constantly growing and new MT paradigms are emerging. In this paper, a systematic literature review has been carried out in order to identify what MT systems are currently most employed, their architecture, the quality assessment procedures applied to determine how they work, and which of these systems offer the best results. The study is focused on the specialised literature produced by translation experts, linguists, and specialists in related fields that include the English–Spanish language combination. Research findings show that neural MT is the predominant paradigm in the current MT scenario, being Google Translator the most used system. Moreover, most of the analysed works used one type of evaluation—either automatic or human—to assess machine translation and only 22% of the works combined these two types of evaluation. However, more than a half of the works included error classification and analysis, an essential aspect for identifying flaws and improving the performance of MT systems.

如今，在我们发现自己处于全球化背景下，语言障碍仍然可能成为获取信息的障碍。有时，仅依靠人工翻译是不可能满足翻译需求的，因此，诸如机器翻译（MT）之类的工具由于具有克服这一问题的潜力而受到欢迎。因此，该领域的研究在不断发展，新的MT范式正在涌现。在本文中，已经进行了系统的文献综述，以识别当前最广泛使用的MT系统，其体系结构，用于确定其工作方式的质量评估程序以及其中哪些系统可提供最佳效果。这项研究的重点是翻译专家，语言学家，以及相关领域的专家，包括英语-西班牙语的组合。研究结果表明，神经MT是当前MT场景中的主要范例，它是Google Translator最常用的系统。此外，大多数被分析的作品使用一种类型的评估（自动或人工）来评估机器翻译，只有22％的作品将这两种评估结合在一起。但是，超过一半的工作包括错误分类和分析，这是识别缺陷和改善MT系统性能的重要方面。大部分经过分析的作品都使用一种类型的评估（自动评估或人工评估）来评估机器翻译，只有22％的作品将这两种评估结合在一起。但是，超过一半的工作包括错误分类和分析，这是识别缺陷和改善MT系统性能的重要方面。大部分经过分析的作品都使用一种类型的评估（自动评估或人工评估）来评估机器翻译，只有22％的作品将这两种评估结合在一起。但是，超过一半的工作包括错误分类和分析，这是识别缺陷和改善MT系统性能的重要方面。