A Simple Baseline to Semi-Supervised Domain Adaptation for Machine Translation
Table 5: Test set BLEU scores after adding extra non-parallel data (“+Data”) from the source WMT domain (“WMT”) or from the target TED domain (“TED”).
['[BOLD] Model', '[BOLD] + Data', '[BOLD] WMT14→', '[BOLD] WMT16→']
One advantage of our method is that it keeps gaining improvement if the non-parallel data get larger. Specifically, we studied the adaptation from the WMT data to TED for both DE-EN and RO-EN language pairs. We consider two sources of extra non-parallel data: source domain and target domain. The source domain data source can be the WMT data itself, whereas for the target domain, we collected an additional dataset of TED talks. Note that for any language pair, we made sure that the transcript of a TED talk only appeared once in either the source or the target side to avoid any parallel sentences. However, if we could not get more non-parallel data from the target domain, those in the source domain that are more readily to be obtained may also potentially improve the adaptation performance. This set of experiments have shown the great potential of our method: we can always seek to collecting more non-parallel data to keep improving the adaptation performance.