There have always been long-winded discussions on the role played by both human and MT in quality translation processes. Which one is better? Or, should they be used in combination to achieve a quality translation? The present paper provides an answer to these matters by means of the calculation of several evaluation metrics to study the quality offered by MT compared to human translation. Moreover, there is a implementation of a new tool based upon a reference model text with some indexes including Narrativity, Readability, Referential Cohesion, Deep Cohesion, and Concreteness, which is compared to the translated text produced by humans. To analyze the appropriateness of the texts as regards reading, a code in Python language has been developed. The first operation carried out by this code is sequencing words of the text to recover the number of paragraphs, sentences, words and syllables in total, and later, it determines the five metrics based on the studies in Coh-Metrix, but simplified. To apply the aforementioned metrics, the following are needed:
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A reference text conforming to a valid corpus,
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A glossary of technical or specific terms which is helping to know which words are specific within a corpus. These terms will not include measurement units nor “words of stop” (prepositions, determiners, etc), and
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A set of connectors allowing to know when, in a sentence, something is being inferred from something previously said.
All previous calculations are complemented with an external evaluation, and that will offer a suitable rubric to assess quality translation.
Keywords: quality in translation processes, specialized translation, english for specific purposes.
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