| Challenge: | Conventional NMT models have difficulty translating words with multiple meanings because of the high ambiguity. |
| Approach: | They propose a neural machine translation model that incorporates named entity (NE) tags of source-language sentences to reduce the difficulty in translating multiple meanings. |
| Outcome: | The proposed model achieves 3.11 point improvement in bilingual evaluation understudy (BLEU) on English-to-Japanese translation task with the ASPEC, and English- to-Bulgarian and English to-Romanian translation tasks with the Europarl corpus. |
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Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, JingBo Zhu
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Learning Source Phrase Representations for Neural Machine Translation (2020.acl-main)
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