Dynamic Data Selection for Neural Machine Translation
Table 2: NMT BLEU comparison between using n-gram LMs and LSTMs for bitext ranking. Selection sizes concern the selected bitext subsets; LMs are created from the exact same in-domain data.
['Selection', 'LM type', 'EMEA', 'Movies', 'TED', 'WMT']
LSTM versus n-gram Before proceeding with dynamic data selection for NMT, we test whether bitext ranking for NMT can be improved using LSTMs rather than conventional n-gram LMs. While results vary among test sets and selection sizes, we observe an average improvement of 0.4 BLEU when using LSTMs instead of n-gram LMs. In all subsequent experiments we use relevance rankings computed with LSTMs instead of n-gram LMs.