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While neural networks have been used extensively to make substantial progressin the machine translation task, they are known for being heavily dependent onthe availability of large amounts of training data. Recent efforts have triedto alleviate the data sparsity problem by augmenting the training data usingdifferent strategies, such as back-translation. Along with the data scarcity,the out-of-vocabulary words, mostly entities and terminological expressions,pose a difficult challenge to Neural Machine Translation systems. In thispaper, we hypothesize that knowledge graphs enhance the semantic featureextraction of neural models, thus optimizing the translation of entities andterminological expressions in texts and consequently leading to a bettertranslation quality. We hence investigate two different strategies forincorporating knowledge graphs into neural models without modifying the neuralnetwork architectures. We also examine the effectiveness of our augmentationmethod to recurrent and non-recurrent (self-attentional) neural architectures.Our knowledge graph augmented neural translation model, dubbed KG-NMT, achievessignificant and consistent improvements of +3 BLEU, METEOR and chrF3 on averageon the newstest datasets between 2014 and 2018 for WMT English-Germantranslation task.

Augmenting Neural Machine Translation with Knowledge Graphs