||Kim, Dongsung. (2021). Automatic scoring system for picture-based English caption writing test adopting deep learning based word-embedding. The Linguistic Association of Korea Journal, 29(2), 1-20. Since human grading of English writing requires substantial resources, many researchers in the area of Computer-Assisted Language Learning (CALL) have been focusing on automatic scoring systems based on natural language processing systems, machine learning, and other automatic processing mechanisms. English Testing Services (ETS) announced several automatic scoring systems for English writing. In this paper, we suggest using a deep learning based automatic scoring system for an English caption writing test. Our method involves using a sentence similarity measurement, which compares different levels of answer sentences with user writing input. We chose different word embedding types (Word2Vec, Word Movers Distance (WMD), Bidirectional Encoder Representations from Transformers (BERT)) and Abstract Meaning Representation (AMR), a linguistic model for comparing semantic differences between two sentences based on semantic representation. Scoring systems should not only satisfy the requirements of complicated scoring rubrics but also meet the conditions of a language proficiency test. Our results show that BERT outperforms three competitive models in predicting accurate scoring levels and also shows the characteristics of the criterion reference which could theoretically express the standards of a language proficiency test.