Before revision:  We apply an Iterative Back Translation (IBT) training scheme on in-domain monolingual data, which repeatedly uses a Transformer-based NMT model to create in-domain pseudo-parallel sentence pairs in one translation direction on the fly and then use them to train the model in the other direction. Evaluated on three domains of German-to-English translation task with no supervised data, this simple technique alone (without any out-of-domain parallel data) can already surpass all previous domain adaptation methods---up to + 9.48 BLEU over the strongest previous method , and up to +27.77 BLEU over the unadapted baseline.
After revision:  We apply an Iterative Back Translation (IBT) training scheme on in-domain monolingual data, which repeatedly uses a Transformer-based NMT model via three training objectives: language modeling, back-translation, and supervised translation. We evaluate this method on two adaptation settings: adaptation between specific domains and adaptation from a general domain to specific domains, and on two language pairs: German to English and Romanian to English. With substantial performance improvement achieved---up to + 9.48 BLEU over the strongest previous method , and up to +27.77 BLEU over the unadapted baseline.