# UPC: An Open Word-Sense Annotated Parallel Corpora for Machine Translation Study
## 6 Conclusions  
In this research, we have proposed large-scale Korean-English and Korean-Vietnamese parallel corpora for training MT models, named UPC. In addition, we annotated the Korean words with sense-codes by our fast and accurate Korean word-sense annotator--UTagger. Both the corpora and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **MT Systems** & & **BLEU** & **TER** \\ \hline \multirow{3}{*}{**Korean-to-English**} & \multirow{3}{*}{SMT} & Original & 18.53 & 70.15 \\ \cline{3-5}  & & Word-sense Ann. & 24.21 & 65.81 \\ \cline{2-5}  & & \multirow{2}{*}{NMT} & Original & 21.28 & 68.19 \\ \cline{3-5}  & & Word-sense Ann. & 27.45 & 60.03 \\ \hline \multirow{3}{*}{**English-to-Korean**} & \multirow{3}{*}{SMT} & Original & 19.18 & 69.89 \\ \cline{3-5}  & & Word-sense Ann. & 20.58 & 69.42 \\ \cline{3-5}  & & Original & 23.57 & 66.38 \\ \cline{3-5}  & & Word-sense Ann. & 25.36 & 63.92 \\ \hline \multirow{3}{*}{**Korean-to-Vietnamese**} & \multirow{3}{*}{SMT} & Original & 20.69 & 68.94 \\ \cline{3-5}  & & Word-sense Ann. & 23.47 & 66.75 \\ \cline{3-5}  & & Original & 24.52 & 64.41 \\ \cline{3-5}  & & Word-sense Ann. & 27.81 & 58.69 \\ \hline \multirow{3}{*}{**Vietnamese-to-Korean**} & \multirow{3}{*}{SMT} & Original & 10.13 & 71.58 \\ \cline{3-5}  & & Word-sense Ann. & 22.31 & 67.05 \\ \cline{1-1} \cline{3-5}  & & Original & 10.49 & 71.12 \\ \cline{1-1} \cline{3-5}  & & Word-sense Ann. & 25.62 & 63.31 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Translation results in BLEU and TER scores. MT = machine translation, SMT = statistical MT, and NMT = neural MT.  
annotator UTagger are respectively available for download at [[https://github.com/haivv/UPC](https://github.com/haivv/UPC)]([https://github.com/haivv/UPC](https://github.com/haivv/UPC)) and [[http://nlplab.ulsalan.ac.kr/doku.php?id=utagger](http://nlplab.ulsalan.ac.kr/doku.php?id=utagger)]([http://nlplab.ulsalan.ac.kr/doku.php?id=utagger](http://nlplab.ulsalan.ac.kr/doku.php?id=utagger)).  
We implemented both SMT and NMT systems using UPC, and the experimental results showed that using these parallel corpora, we could train high-quality NMT systems, and the Korean word-sense annotation could improve the translation quality of both the SMT and NMT systems, especially in the Korean-to-English and Korean-to-Vietnamese translation directions.  
In our future works, we intend to gather more parallel corpora related to the Korean language, such as Korean-French and Korean-Chinese. We also plan to apply Korean name entity recognition and syntax to Korean text in the parallel corpora to build more accurate MT systems that can translate from or to Korean.  
Data curation, V-H.V. and Q.-P.N.; formal analysis, V.-H.V.; funding acquisition, C.-Y.O.; methodology, V.-H.V.; project administration, C.-Y.O.; resources, Q.-P.N.; software, Q.-P.N. and J.-C.S.; validation, V.-H.V., Q.-P.N., and C.-Y.O.; writing--draft, V.-H.V. and Q.-P.N.; and writing--review and editing, J.-C.S. and C.-Y.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2013-0-00131, Development of Knowledge Evolutionary WiseQA Platform Technology for Human Knowledge Augmented Services) and the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2017M3C4A7068187).  
The authors declare no conflicts of interest.