Self-Induced Curriculum Learning in Neural Machine Translation
Table 2: BLEU, TER and METEOR scores of the self-supervised NMT systems trained on the three en–{fr,de,es} comparable WPs and tested on NT13/NT14. We compare the performance with current state-of-the-art (SOTA) systems in supervised NMT (Edunov et al., ) / unsupervised NMT (Lample et al., 2018b) / unsupervised NMT+SMT (Artetxe et al., 2019).
['[EMPTY]', 'SS-NMT L1–L2', 'SS-NMT L1–L2', 'SS-NMT L1–L2', 'SS-NMT L2–L1', 'SS-NMT L2–L1', 'SS-NMT L2–L1', 'SOTA L1–L2', 'SOTA L2–L1']
SS-NMT is on par with the current SOTA in unsupervised NMT, slightly outperforming it by ∼3-4 BLEU points in en–fr while having a lower performance on en–de (∼3 BLEU). Notice that unsupervised systems such as Lample et al. However, once unsupervised NMT is combined with statistical methods, these outperform SS-NMT by large margins, i.e. ∼6 for en–fr and ∼5–9 BLEU for en– de. In a recent study by Wu et al. They achieve similar BLEU scores as SS-NMT for en2es (28.1) and es2en (27.6) on NT13; notably SS-NMT trains on comparable data only.