Extract and Attend: Improving Entity Translation in Neural Machine
  Translation
Zixin Zeng Rui Wang Yichong Leng Junliang Guo Xu Tan Tao Qin Tie-yan Liu
While Neural Machine Translation(NMT) has achieved great progress in recent
years, it still suffers from inaccurate translation of entities (e.g.,
person/organization name, location), due to the lack of entity training
instances. When we humans encounter an unknown entity during translation, we
usually first look up in a dictionary and then organize the entity translation
together with the translations of other parts to form a smooth target sentence.
Inspired by this translation process, we propose an Extract-and-Attend approach
to enhance entity translation in NMT, where the translation candidates of
source entities are first extracted from a dictionary and then attended to by
the NMT model to generate the target sentence. Specifically, the translation
candidates are extracted by first detecting the entities in a source sentence
and then translating the entities through looking up in a dictionary. Then, the
extracted candidates are added as a prefix of the decoder input to be attended
to by the decoder when generating the target sentence through self-attention.
Experiments conducted on En-Zh and En-Ru demonstrate that the proposed method
is effective on improving both the translation accuracy of entities and the
overall translation quality, with up to 35% reduction on entity error rate and
0.85 gain on BLEU and 13.8 gain on COMET.