# Mixup Based Cross-Consistency Training for Named Entity Recognition
## 5 Conclusions  
Mixup, which generates a new data point from mixing two different data points, was proposed as a data augmentation technique in computer vision. Although several prior studies showed the effectiveness of mixup, it would lead to the NER model training in an undesirable direction if we use it directly on NER examples. Based on the hindernce this mixup results in when applied on NER examples, we thought mixup could be a perturbation on NER examples and used it as a perturbation function for consistency  
Figure 6: The trends of the test set’s span-level F1-score according to the size of the strongly labeled dataset. We described detailed numbers in Appendix A.  
Figure 7: t-SNE visualization of BC5 CDR-chem. The representations are extracted from language model \(F\). (**a**) shows the result of the BERT baseline, and (**b**) shows the result of the proposed framework. regularization. In this study, we proposed a novel framework that uses the mixup based the consistency regularization technique for Named Entity Recognition tasks. To validate our proposed framework, we conducted experiments on domain-specific NER benchmark datasets. The experimental results demonstrated that the mixup for weakly labeled data would act as a perturbation function for NER examples, especially on low-resource environments. We expect that the proposed framework can mitigate efforts for building datasets for domain-specific NER tasks. Conceptualization, G.Y. and J.R.; methodology G.Y. ; software, G.Y. ; validation, B.Y. ; investigation, S.J. and D.K. ; data curation, G.Y. ; writing--original draft preparation, G.Y. ; writing--review and editing, G.Y., B.Y., S.J. and D.K. ; visualization, G.Y. and D.K. ; supervision, J.R.; project administration, J.R.; funding acquisition, J.R. All authors have read and agreed to the published version of the manuscript. This work was supported by the Technology development Program (S2957729) funded by the Ministry of SMEs and Startups (MSS, Sejong, Korea). Not applicable. [leftmargin=*]
**Index Availability Statement:** Publicly available datasets were analyzed in this study. For biomedical domains, the dataset can be found here: [[https://github.com/cambridgeltl/MTL-Bioinformatics-2016](https://github.com/cambridgeltl/MTL-Bioinformatics-2016)]([https://github.com/cambridgeltl/MTL-Bioinformatics-2016](https://github.com/cambridgeltl/MTL-Bioinformatics-2016)), accessed on 29 October 2022. For weakly labeled data, we used PubMed 2021 baseline ([[https://ftp.ncbi.nlm.nih.gov/pubmed/](https://ftp.ncbi.nlm.nih.gov/pubmed/)]([https://ftp.ncbi.nlm.nih.gov/pubmed/](https://ftp.ncbi.nlm.nih.gov/pubmed/)), downloaded on 11 February 2022 and accessed on 29 October 2022) as the biomedical raw corpus. we used a combination of the MeSH database ([[https://www.nlm.nih.gov/databases/download/mesh.html](https://www.nlm.nih.gov/databases/download/mesh.html)]([https://www.nlm.nih.gov/databases/download/mesh.html](https://www.nlm.nih.gov/databases/download/mesh.html)), downloaded on 11 February 2022 and accessed on 29 October 2022) and CTD Chemical and Disease vocabulary ([[http://ctdbase.org/downloads/](http://ctdbase.org/downloads/)]([http://ctdbase.org/downloads/](http://ctdbase.org/downloads/)), downloaded on 11 February 2022 and accessed on 29 October 2022) to build a entity dictionary. For tech term domains, the dataset can be found here: [[https://altcl.qcri.org/semeval2014/task4/index.php?id=data-and-tools](https://altcl.qcri.org/semeval2014/task4/index.php?id=data-and-tools)]([https://altcl.qcri.org/semeval2014/task4/index.php?id=data-and-tools](https://altcl.qcri.org/semeval2014/task4/index.php?id=data-and-tools)), accessed on 29 October 2022. For the tech term dictionary, we searched public websites ([[https://www.computerhope.com/jargon.htm](https://www.computerhope.com/jargon.htm)]([https://www.computerhope.com/jargon.htm](https://www.computerhope.com/jargon.htm)), accessed on 29 October 2022).