## 5 Conclusions  
We proposed a relation extraction model to find all possible relations among multiple entities in a sentence simultaneously. The proposed model is based on pointer networks with multi-head attention mechanisms. To extract all possible relations from a sentence, we modified a single decoder into a dual decoder. In the dual decoder, the object decoder extracts _n_-to-_1_ subject-object relations, and the subject decoder extracts _1_-to-_n_ subject-object relations. The results from the experiments with the ACE-2005 corpus and the NYT corpus confirmed that the proposed model shows an improvement in performance. Our future work will focus on an end-to-end model that directly extracts entities and their relations. In addition, we will focus on a method for improving performance using a large-scale language model like BERT [25].  
Conceptualization, H.K.; methodology, H.K.; software, S.P.; validation, S.P.; formal analysis, H.K.; investigation, H.K.; resources, S.P.; data curation, S.P.; writing--original draft preparation, S.P.; writing--review and editing, H.K.; visualization, H.K.; supervision, H.K.; project administration, H.K.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2013-0-00131, Development of Knowledge Evolutionary WiseQA Platform Technology for Human Knowledge Augmented Services). This work was also supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No.2020-0-00368, A Neural-Symbolic Model for Knowledge Acquisition and Inference Techniques).  
We especially thank the members of the NLP laboratory at Kangwon National University and Konkuk University for their technical support.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Sentence** & **Correct Relation** & **Predicted Relation** \\ \hline Iraqi forces responded with artillery fire & \begin{tabular}{c} \{Iraqi forces, ART, artillery\} \\ [Iraqi forces, GEN-AFF, Iraqi] \\ \end{tabular} & \begin{tabular}{c} \{Iraqi forces, PART-WHOLE, Iraqi\} \\ \end{tabular} \\ \hline It is the first time they have had & & \\ freedom of movement with cars & \begin{tabular}{c} \{they, ART, cars\} \\ \{they, ART, weapons\} \\ \end{tabular} & \begin{tabular}{c} \{they, ART, cars\} \\ \end{tabular} \\ and weapons since the start of the & \begin{tabular}{c} \{they, ART, weapons\} \\ \end{tabular} & \begin{tabular}{c} \{they, ART, cars\} \\ \end{tabular} \\ \hline It was in northern Iraq today that & \
\begin{tabular}{c} \{Kurdish fighters, PHYS, the site\} \\ \{the site, PHYS, Chamchamal\} \\ \{Kurdish, GEN-AFF, Kurdish\} \\ \end{tabular} &
\begin{tabular}{c} \{Kurdish fighters, PHYS, the site\} \\ \{the site, PHYS, Chamchamal\} \\ \{the site, PART-WHOLE, northern \\ Iraq\} \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 8: Main reasons for errors in the ACE-2005 corpus (underline denotes incorrect results). ART: artifact; GEN-AFF: general entity-affiliation; PHYS: physical; PART-WHOLE: part of whole.