Translation tools and technologies have gained increasing importance in the translation sector over the years, but until now have been little applied to the specific field of survey translation. To rectify this, the SERISS project held a symposium on “synergies between survey translation and developments in language and translation sciences” at University Pompeu Fabra (UPF) in Barcelona from 1-2 June 2017. The meeting was attended by delegates from the three main surveys involved in the SERISS project (the European Social Survey (ESS), Survey of Health, Ageing and Retirement in Europe (SHARE ERIC), and the European Values Study (EVS)), as well as by representatives of translation technology companies (cApStAn, Kantar Public and CentERdata) and by a leading academic in automated translation, Professor Toni Badia of UDF.
I was invited to report on my recent work on evaluating ELSST translation quality (see First results of SERISS project) and to consider prospects for automating the translation and evaluation of thesaurus terms.
Two general recommendations emerged from the symposium that are relevant to the translation of ELSST, as well as to survey translation.
First, it is important to analyse the whole life cycle of a product (not just the translation process) and to understand all the steps involved. Action taken prior to the translation phase has an impact on the translation process. We know this in ELSST, which is why we try, when creating new concepts, to choose source language labels and scope notes that will not present problems to the translators.
Second, it is critical to identify which steps a machine can perform better than humans. In the case of survey translation, this includes recognising questions that have not changed since the last wave of a survey, and which thus do not need to be retranslated. It also includes consistency checking in the quality assessment phase. Consistency checking would also be useful in ELSST, to make sure that source language terms that appear within other terms are given the same translation in the corresponding target language terms, where appropriate.
Delegates agreed that translation memories would be helpful to survey translation, since they store previously translated text which can be reused.The SERISS project is currently using CentERdata’s Translation Management Tool (TMT) for managing translation of its questionnaires. It is not particularly relevant to ELSST right now, but we shall see how it develops in the course of the project. The plan is to integrate it with a translation memory in the near future.
Toni Badia proposed that machine translation was mature enough to be able to offer a first draft of survey questions, if human resources were not available. He mentioned that a good starting point was phrase-based statistical machine translation systems, such as Moses, but noted that the paradigm is shifting towards neural machine translation which promises better quality. This is certainly something we could also consider for ELSST.
Another recommendation of the symposium was for translators of the different surveys to collaborate with each other. Each has a list of well-known translation problems. These problems would interest ELSST developers also, so we shall ask to be included in any future collaboration.