Knowledge Graphs Enhanced Neural Machine Translation
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 4039-4045. https://doi.org/10.24963/ijcai.2020/559
Knowledge graphs (KGs) store much structured information on various entities, many of which are not covered by the parallel sentence pairs of neural machine translation (NMT). To improve the translation quality of these entities, in this paper we propose a novel KGs enhanced NMT method. Specifically, we first induce the new translation results of these entities by transforming the source and target KGs into a unified semantic space. We then generate adequate pseudo parallel sentence pairs that contain these induced entity pairs. Finally, NMT model is jointly trained by the original and pseudo sentence pairs. The extensive experiments on Chinese-to-English and Englishto-Japanese translation tasks demonstrate that our method significantly outperforms the strong baseline models in translation quality, especially in handling the induced entities.
Natural Language Processing: Machine Translation
Natural Language Processing: Natural Language Generation
Knowledge Representation and Reasoning: Semantic Web