Abstract
An important prerequisite for data-driven Machine Translation (MT) systems is the availability of high-quality training data. Corpus-based MT systems requiring domain specificity additionally require the selection of a large training dataset and application of proper domain adaptation techniques. This paper demonstrates the portability of MT systems to multiple domains under low-resource conditions for the target language, where target-language domain data are not available. We refer to this case as a bilingually low-resource language pair. We compare the performance of an attention-based Neural MT (NMT) system to that of a phrase-based Statistical MT (SMT) system under this condition, training each on parallel corpora consisting of different domains. Experimental results on Spanish-Farsi as a bilingually low-resource language pair show that the SMT paradigm still outperforms that of NMT.
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