KoEn-MedBERT: Multilingual Medical Language Model for Analyzing Korean and English Medical Texts
Authors: Eunbeen Jo, Hyung Joon Joo


Reviewer paper summaries: The paper proposes a multilingual medical language model using Korean and English corpus by training BERT from scratch.
They evaluate the model's performance by measuring the similarity of the embedding vectors of pairs of related medical terms and medical language inference tasks translated into Korean.
Summary of strengths: Medical text is often written in the local language and English; the proposed multilingual BERT model trained explicitly in these two languages in the medical domain will be appropriate for many downstream tasks.
Summary of weaknesses: The proposed model performs on par with a single-language model in both language benchmark settings.
The method to develop the multilingual model is a standard approach with little novelty.
Comments, suggestions and typos: The format of the paper does not fully conform to the ACL requirements. 
-no page numbers.
-line number is not a three-digit
Overall assessment scores: 2.5