• Proceedings of the Korean Society for Language and Information Conference
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• 1995.02a
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• pp.115-120
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• 1995

• Proceedings of the Korean Society for Language and Information Conference
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• 1995.02a
• /
• pp.173-184
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• 1995

• Proceedings of the Korean Society for Language and Information Conference
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• 1995.02a
• /
• pp.243-248
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• 1995

• Proceedings of the Korean Society for Language and Information Conference
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• 1995.02a
• /
• pp.267-272
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• 1995

• Proceedings of the Korean Society for Language and Information Conference
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• 1996.02a
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• pp.415-424
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• 1996

• Proceedings of the Korean Society for Language and Information Conference
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• 1996.02a
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• pp.119-128
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• 1996

• The Korean Journal of Elementary Counseling
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• v.10 no.1
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• pp.39-56
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• 2011

The purpose of this study is to draw the elements of encouraging language from the literatures of encouragement and develop the encouraging language model for elementary school teachers. To achieve this, first of all, the literatures about the methods of encouragement were collected. And then the collected literatures were categorized according to the main concept which each literature contained. As a result, 5 categories and 17 subcategories were drawn. 5 categories were valuing a child as a human-being itself, trusting a child, thinking rationally about a child's mistakes, giving a feedback about a child's behaviors non-evaluatively, and reflecting a child's positive feeling. These 5 categories were established as the elements of encouraging language. The encouraging language model was developed on the bases of the 5 elements of encouraging language. The model was constructed of the examples of encouraging language in various classroom situations. The model contains various situations which elementary school teachers often confront in their classrooms. And the model shows the examples of encouraging language proper for each situation. Every example was constructed on the bases of the elements of encouraging language.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.17-22
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• 2021

It is difficult for the hearing impaired to use the counseling service without sign language interpretation. Due to the shortage of sign language interpreters, it takes a lot of time to connect to sign language interpreters, or there are many cases where the connection is not available. Therefore, in this paper, we propose a system that captures sign language as an image using OpenCV and CNN (Convolutional Neural Network), recognizes sign language motion, and converts the meaning of sign language into textual data and provides it to users. The counselor can conduct counseling by reading the stored sign language translation counseling contents. Consultation is possible without a professional sign language interpreter, reducing the burden of waiting for a sign language interpreter. If the proposed system is applied to counseling services for the hearing impaired, it is expected to improve the effectiveness of counseling and promote academic research on counseling for the hearing impaired in the future.

• Smart Media Journal
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• v.12 no.1
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• pp.32-46
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• 2023

Natural Language Processing (NLP) has grown tremendously in recent years. Typically, bilingual, and multilingual translation models have been deployed widely in machine translation and gained vast attention from the research community. On the contrary, few studies have focused on translating between spoken and sign languages, especially non-English languages. Prior works on Sign Language Translation (SLT) have shown that a mid-level sign gloss representation enhances translation performance. Therefore, this study presents a new large-scale Korean sign language dataset, the Museum-Commentary Korean Sign Gloss (MCKSG) dataset, including 3828 pairs of Korean sentences and their corresponding sign glosses used in Museum-Commentary contexts. In addition, we propose a translation framework based on self-supervised learning, where the pretext task is a text-to-text from a Korean sentence to its back-translation versions, then the pre-trained network will be fine-tuned on the MCKSG dataset. Using self-supervised learning help to overcome the drawback of a shortage of sign language data. Through experimental results, our proposed model outperforms a baseline BERT model by 6.22%.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.273-283
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• 2022

Banking and finance is a broad term that incorporates a variety of smaller, more specialized subjects such as corporate finance, tax finance, and insurance finance. A virtual assistant that assists users in searching for information about banking and finance terms might be an extremely beneficial tool for users. In this study, we explored the process of searching for information, seeking opportunities, and developing a virtual assistant in the first stages of starting learning and understanding Vietnamese to increase effectiveness and save time, which is also an innovative business practice in Use-case Vietnam. We built the FIBA2020 dataset and proposed a pipeline that used Natural Language Processing (NLP) inclusive of Natural Language Understanding (NLU) algorithms to build chatbot applications. The open-source framework RASA is used to implement the system in our study. We aim to improve our model performance by replacing parts of RASA's default tokenizers with Vietnamese tokenizers and experimenting with various language models. The best accuracy we achieved is 86.48% and 70.04% in the ideal condition and worst condition, respectively. Finally, we put our findings into practice by creating an Android virtual assistant application using the model trained using Whitespace tokenizer and the pre-trained language m-BERT.