Translation Memories as Baselines for Low-Resource Machine Translation
Low-resource machine translation research often requires building baselines to benchmark estimates of progress in translation quality. Neural and statistical phrase-based systems are often used with out-of-the-box settings to build these initial baselines before analyzing more sophisticated approaches, implicitly comparing the first machine translation system to the absence of any translation assistance. We argue that this approach overlooks a basic resource: if you have parallel text, you have a translation memory. In this work, we show that using available text as a translation memory baseline against which to compare machine translation systems is simple, effective, and can shed light on additional translation challenges.
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