Data Diversification: An Elegant Strategy For Neural Machine Translation
Table 12: Improvements of data diversification under conditions maximum likelihood and beam search in the IWSLT’14 English-German and German-English.
['[BOLD] Task', '[BOLD] Baseline', '[BOLD] Ours  [BOLD] Beam=1', '[BOLD] Ours  [BOLD] Beam=5']
We hypothesized that beam search will generate more diverse synthetic translations of the original dataset, thus increases the diversity and improves generalization. We tested this hypothesis by using greedy decoding (beam size=1) to generate the synthetic data and compare its performance against beam search (beam size=5) counterparts. We also used the IWSLT’14 English-German and German-English as a testbed. Note that for testing with the final model, we used the same beam search (beam size5) procedure for both cases.