| Abstract | This document reports on the results of Phase 1 of a research project to assess the feasibility of using Artificial Intelligence (AI) techniques to automatically evaluate translation quality in Statement of Merit Criteria (SOMC) data. This project is carried out by the National Research Council (NRC), in collaboration with the Public Service Commission of Canada (PSC). The PSC manages job advertisement for the Canadian government, and measures the overall compliance of job postings with official languages requirements. The main problem in this regard are ‘equivalence errors’ in SOMC’s, i.e. differences in the meaning of the English and French texts that can have an impact on applicants’ access to federal public jobs, as well as on the outcome of appointment processes. To support this project, PSC has provided NRC with a sample of SOMC data, in which ‘equivalence errors’ have been marked by PSC auditors. Analysis of this data reveals that about 10% of all SOMC sentences display equivalence issues. NRC proposed an AI method to assign quality scores to individual pairs of English-French sentences. Experiments on PSC data show that by using this method in an interactive setting, it would be possible to detect as much as 75% of all equivalence errors by controlling only 28% of the text. This method could be efficiently implemented as a software service for PSC, and various techniques exist that could further improve its performance. |
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