As expected, experiments on 10× larger Europarl datasets for 3 languages show that neural models outperform -gram models in less challenging data-intensive scenarios.
training on large datasets comes at the expense of training efficiency for neural models: e.g., according to Figure 1 training non-Bayesian -grams is around 15,000× quicker than training neural models.