Machine versus corpus-based translation of multiword terms

Abstract

Abstract Machine translation (MT) post-editing is an increasingly common practice in the translation industry which is also slowly being applied in the development of terminological resources. However, more studies have been devoted to analyze the practice in a translation scenario than in a terminographic context. Consequently, term-oriented post-editing guidelines are a current need if terminographers are also to become post-editors. With a view to enhancing the multilingual representation of environmental multiword terms (MWTs) in terminological resources, we analyze English–Spanish MWT translation in various generic MT systems. Our aims are: (1) to evaluate MT output in order to check whether it can be of any help to terminographers’ work; (2) to develop an error typology in order to raise terminographers’ awareness; and (3) to use the error typology to sketch a series of basic pre-editing and post-editing rules in a terminographic scenario. A comparison of MT output with the equivalents found in a comparable corpus is also presented. Even though MT often presents errors or unidiomatic choices, it can still serve as a basis for human post-editing, and provided that post-editors are familiarized with the potential errors. Comparable corpora, on the other hand, offer better results, but searches are more time-consuming and equivalents are not always available.