# Named Entity Correction in Neural Machine Translation Using the Attention Alignment Map
## 6 Conclusions  
Even the NMT models that show state-of-the-art performance on multiple machine translation tasks are still limited when dealing with OOV and rarely occurring words. We found that the problem is particularly relevant for the translation of historical documents with multiple named entities. In this paper, we proposed a postprocessing approach to address this limitation. The proposed method corrects the machine translation output using the NER model and the attention map. The NER model finds named entities in the source sentence, and the attention map aligns the located named entities with the tokens in the translated sentence. Next, we assumed that the tokens aligned with the source named entities were mistranslated, and we replaced them using the look-up table or an identity copy. Experiments with various target vocabulary sizes in Section 4 demonstrated that our method is effective in the task of translation of historical documents from Chinese to Korean. Using the proposed NER method, the machine translation performance was improved up to 3.70 in terms of the BLEU score (35.83 to 39.53) in seq2 seq translation models and up to 3.17 (33.90 to 37.07) in transformer models. Moreover, there was no BLEU score degradation due to the proposed method. The proposed method can be applied to an existing NMT model that uses the attention mechanism without retraining the model, if an NER model exists for the source language. Our method can be successfully applied not only to Chinese-to-Korean translation, but also to other language pairs. In our future work, we plan to explore this direction.  
Conceptualization, M.L., [PERSON] ([PERSON]) and G.-J.J.; methodology, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]) and G.-J.J.; formal analysis, [PERSON] ([PERSON]) and M.L.; investigation, [PERSON] ([PERSON]); resources, G.-J.J. and J.L. ([PERSON]); data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]) and G.-J.J.; writing--review and editing, M.L. and J.L. ([PERSON]); visualization, [PERSON] ([PERSON]); supervision, G.-J.J.; project administration, G.-J.J.; funding acquisition, G.-J.J. All authors read and agree to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2017M3C1B6071399), and by the Technology Innovation Program (20016180, Forecast of overseas inflow of new infectious diseases and development of intelligent blocking technology) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Source** & \\
**Truth** & \\
**English Translation** & \\  & The king wort to [PERSON] and conducted a So Dae. \\  & The king ordered the subjects to read \(\ll\)Shim Gyung\(\gg\). \\
**Baseline** & \\
**Proposed** & \\
**NER output** & \\ \hline
**Source** & \\
**Truth** & \\
**English Translation** & \\
**Baseline** & \\
**Proposed** & \\
**NER output** & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Weaknesses of the proposed method. _Source_: input sentence. _Truth_: ground truth. _Baseline_: outputs of the NMT models. _Proposed_: results of our approach. _NER output_: outputs of the NER model. Underlined words: named entities. Green-colored tokens: named entities for the names of books. Blue-colored: named entities for the names of place names.