• Journal of Information Technology Services
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• v.21 no.2
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• pp.85-95
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• 2022

This study analyzed the translation error factors of machine translation services such as Naver Papago and Google Translate through Self-Attention path visualization. Self-Attention is a key method of the Transformer and BERT NLP models and recently widely used in machine translation. We propose a method to explain translation error factors of machine translation algorithms by comparison the Self-Attention paths between ST(source text) and ST'(transformed ST) of which meaning is not changed, but the translation output is more accurate. Through this method, it is possible to gain explainability to analyze a machine translation algorithm's inside process, which is invisible like a black box. In our experiment, it was possible to explore the factors that caused translation errors by analyzing the difference in key word's attention path. The study used the XLM-RoBERTa multilingual NLP model provided by exBERT for Self-Attention visualization, and it was applied to two examples of Korean-Chinese and Korean-English translations.

• Journal of the Korean Regional Science Association
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• v.39 no.2
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• pp.47-61
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• 2023

Satisfaction on the residential environment is a major factor influencing the choice of residence and migration, and is directly related to the quality of life in the city. As online services of real estate increases, people's evaluation on the residential environment can be easily checked and it is possible to analyze their satisfaction and its determining factors based on their evaluation. This means that a larger amount of evaluation can be used more efficiently than previously used methods such as surveys. This study analyzed the residential environment reviews of about 30,000 apartment residents collected from 'Zigbang', an online real estate service in Seoul. The apartment review of Zigbang consists of an evaluation grade on a 5-point scale and the evaluation content directly described by the dweller. At first, this study labeled apartment reviews as positive and negative based on the scores of recommended reviews that include comprehensive evaluation about apartment. Next, to classify them automatically, developed a model by using Bidirectional Encoder Representations from Transformers(BERT), a deep learning-based natural language processing model. After that, by using SHapley Additive exPlanation(SHAP), extract word tokens that play an important role in the classification of reviews, to derive determining factors of the evaluation of the residential environment. Furthermore, by analyzing related keywords using Word2Vec, priority considerations for improving satisfaction on the residential environment were suggested. This study is meaningful that suggested a model that automatically classifies satisfaction on the residential environment into positive and negative by using apartment review big data and deep learning, which are qualitative evaluation data of residents, so that it's determining factors were derived. The result of analysis can be used as elementary data for improving the satisfaction on the residential environment, and can be used in the future evaluation of the residential environment near the apartment complex, and the design and evaluation of new complexes and infrastructure.