Abstract
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.
Highlights
Language barriers can be an obstacle to accessing information in the globalised context in which we find ourselves
The annotation and classification of translation errors is fundamental for contributing to the improvement of Machine Translation (MT) systems, in order to understand the criteria of human metrics—given that this type of assessment has an element of subjectivity—and to optimise the post-editing process (Costa et al, 2015; Popovic, 2018)
Following the systematic review of the publications that make up our study sample it is observed, firstly, that neural MT is the predominant model in the current MT scenario
Summary
Language barriers can be an obstacle to accessing information in the globalised context in which we find ourselves Such is the abundance of information generated that it is on occasions impossible to satisfy the demand for translations by relying solely on professional human translators (Lagarda et al, 2015; Way, 2018). One of the MT battlefields, refers to the quality of the product it creates, which is generally inferior to that reached by professional human translations. It should be possible to determine the quality of how an MT system works, and its impact on the workflow of professional translators, in an objective manner This will require both automated and human metrics that need, in addition, to take into account the human post-editing that is usually necessary for MT. The annotation and classification of translation errors is fundamental for contributing to the improvement of MT systems, in order to understand the criteria of human metrics—given that this type of assessment has an element of subjectivity—and to optimise the post-editing process (Costa et al, 2015; Popovic, 2018)
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