Incorporating Named Entity Information into Neural Machine Translation

Most neural machine translation (NMT) models normally take the subword-level sequence as input to address the problem of representing out-of-vocabulary words (OOVs). However, using subword units as input may omit the information carried by larger text granularity, such as named entities, which leads to a loss of important semantic information. In this paper, we propose a simple but effective method to incorporate the named entity (NE) tags information into the Transformer translation system. The encoder of our proposed model takes both the subwords and the NE tags of source sentences as inputs. Furthermore, we introduce a novel entity-aligned attention mechanism to make full use of the chunk information of NE tags. The proposed approach can be easily integrated into the existing framework of Transformer. Experimental results on two public translation tasks demonstrate that our proposed method can achieve significant translation improvements over the basic Transformer model and also outperforms the existing competitive systems.