Augmenting Neural Machine Translation with Knowledge Graphs
Table 1: Results of RNN models in BLEU (Bleu), METEOR (Met), chrF3 on WMT newstest datasets. Word → word-based models, CopyM → Copy Mechanism and BPE32 → BPE models.
['[EMPTY]', '[BOLD] Models', '[BOLD] newstest2014 Bleu', '[BOLD] newstest2014 Met', '[BOLD] newstest2014 chrF3', '[BOLD] newstest2015 Bleu', '[BOLD] newstest2015 Met', '[BOLD] newstest2015 chrF3', '[BOLD] newstest2016 Bleu', '[BOLD] newstest2016 Met', '[BOLD] newstest2016 chrF3', '[BOLD] newstest2017 BBleu', '[BOLD] newstest2017 Met', '[BOLD] newstest2017 chrF3', '[BOLD] newstest2018 Bleu', '[BOLD] newstest2018 Met', '[BOLD] newstest2018 chrF3']
Overall results KG-NMT (SemKGE) outperformed KG-NMT (EL+KGE) by around +1.3 in BLEU and chrF3, while we observe a +2 point improvement for METEOR. id13 also presented consistent improvements showing that segmentation on labels of KG-NMT (SemKGE) model worked.