Data Troubles in Sentence Level Confidence Estimation for Machine Translation
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The paper investigates the feasibility of confidence estimation for neural machine translation models operating at the high end of the performance spectrum. As a side product of the data annotation process necessary for building such models we propose sentence level accuracy SACC as a simple, self-explanatory evaluation metric for quality of translation. Experiments on two different annotator pools, one comprised of non-expert (crowd-sourced) and one of expert (professional) translators show that SACC can vary greatly depending on the translation proficiency of the annotators, despite the fact that both pools are about equally reliable according to Krippendorff's alpha metric; the relatively low values of inter-annotator agreement confirm the expectation that sentence-level binary labeling good / needs work for translation out of context is very hard. For an English-Spanish translation model operating at SACC = 0.89 according to a non-expert annotator pool we can derive a confidence estimate that labels 0.5-0.6 of the good translations in an "in-domain" test set with 0.95 Precision. Switching to an expert annotator pool decreases SACC dramatically: 0.61 for English-Spanish, measured on the exact same data as above. This forces us to lower the CE model operating point to 0.9 Precision while labeling correctly about 0.20-0.25 of the good translations in the data. We find surprising the extent to which CE depends on the level of proficiency of the annotator pool used for labeling the data. This leads to an important recommendation we wish to make when tackling CE modeling in practice: it is critical to match the end-user expectation for translation quality in the desired domain with the demands of annotators assigning binary quality labels to CE training data.
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