Extract and Attend: Improving Entity Translation in Neural Machine Translation

• URL: http://arxiv.org/abs/2306.02242v1
• Date: Sun, 4 Jun 2023 03:05:25 GMT
• Title: Extract and Attend: Improving Entity Translation in Neural Machine Translation
• Authors: Zixin Zeng, Rui Wang, Yichong Leng, Junliang Guo, Xu Tan, Tao Qin, Tie-yan Liu
• Abstract summary: We propose an Extract-and-Attend approach to enhance entity translation in NMT. The proposed method is effective on improving both the translation accuracy of entities and the overall translation quality.
• Score: 141.7840980565706
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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