• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.27-35
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• 2021

Automatic post editing is a research field that aims to automatically correct errors in machine translation results. This research is mainly being focus on high resource language pairs, such as English-German. Recent APE studies are mainly adopting transfer learning based research, where pre-training language models, or translation models generated through self-supervised learning methodologies are utilized. While translation based APE model shows superior performance in recent researches, as such researches are conducted on the high resource languages, the same perspective cannot be directly applied to the low resource languages. In this work, we apply two transfer learning strategies to Korean-English APE studies and show that transfer learning with translation model can significantly improves APE performance.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.69-80
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• 2021

Neural machine translation using deep neural network has emerged as a mainstream research, and an abundance of investment and studies on model structure and parallel language pair have been actively undertaken for the best performance. However, most recent neural machine translation studies pass along decoding strategy to future work, and have insufficient a variety of experiments and specific analysis on it for generating language to maximize quality in the decoding process. In machine translation, decoding strategies optimize navigation paths in the process of generating translation sentences and performance improvement is possible without model modifications or data expansion. This paper compares and analyzes the significant effects of the decoding strategy from classical greedy decoding to the latest Dynamic Beam Allocation (DBA) in neural machine translation using a sequence to sequence model.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.13-20
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• 2022

This study is to develop a named entity recognition model specialized in criminal investigation domains using deep learning techniques. Through this study, we propose a system that can contribute to analysis of crime for prevention and investigation using data analysis techniques in the future by automatically extracting and categorizing crime-related information from text-based data such as criminal judgments and investigation documents. For this study, the criminal investigation domain text was collected and the required entity name was newly defined from the perspective of criminal analysis. In addition, the proposed model applying KoELECTRA, a pre-trained language model that has recently shown high performance in natural language processing, shows performance of micro average(referred to as micro avg) F1-score 98% and macro average(referred to as macro avg) F1-score 95% in 9 main categories of crime domain NER experiment data, and micro avg F1-score 98% and macro avg F1-score 62% in 56 sub categories. The proposed model is analyzed from the perspective of future improvement and utilization.

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.249-256
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• 2018

This study aims to explore efficient foreign language learning paradigm with a focus on reading fluency and accuracy. From a perspective of language acquisition in the foreign language context, the priority in the L2 learning between accuracy and fluency has been a very important issue. Fluency becomes an important issue due to many researchers' interests in the L1 and L2 classroom. Although both accuracy and fluency are crucial, the paradigm shift from fluency to accuracy is necessary in the foreign language teaching. In this context, as an alternative methodology for L2 learners' fluency, the extensive reading approach is provided. A number of studies have suggested that extensive reading program could lead to improvement of L2 learners' reading rate and is an effective approach to improving general language proficiency.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.167-172
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• 2019

The purpose of this study was to investigate the effect of computer based cognitive rehabilitation program on the generative naming. Twenty - one patients were assigned to the CoTras program and eight were treated with traditional face - to - face language rehabilitation such as paper and table activities. The experimental group and the control group performed sequential language recall memory training, association memory recall training, language categorization memory training, and language integrated memory training for 12 weeks. The Welch's robust ANCOVA showed significant differences in mean fluency and MMSE-K changes (p<0.05). On the other hand, phonemic fluency increased significantly after 12 weeks of treatment compared to baseline in both experimental and control groups, but there was no statistically significant difference between treatment groups. The results of this study suggest that the computer based cognitive rehabilitation program may be more effective in improving the semantic fluency than the conventional cognitive-linguistic rehabilitation.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.13-21
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• 2022

Recent deep learning-based research shows excellent performance in most natural language processing (NLP) fields with pre-trained language models. In particular, the auto-encoder-based language model proves its excellent performance and usefulness in various fields of Korean language understanding. However, the decoder-based Korean generative model even suffers from generating simple sentences. Also, there is few detailed research and data for the field of conversation where generative models are most commonly utilized. Therefore, this paper constructs multi-turn dialogue data for a Korean generative model. In addition, we compare and analyze the performance by improving the dialogue ability of the generative model through transfer learning. In addition, we propose a method of supplementing the insufficient dialogue generation ability of the model by extracting recommended response candidates from external knowledge information through a retrival model.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.297-304
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• 2021

This study aims not only to access the visual thought-oriented approach that has been implemented in established art therapy and education but also to integrate language education and therapeutic approach to support the development of school-age children. Thus, text mining technique was applied to search for areas where different areas of language and art can be integrated. This research was conducted in accordance with the procedure of basic research, preliminary DB construction, text screening, DB pre-processing and confirmation, stop-words removing, text mining analysis and the deduction about the convergent areas. These results demonstrated that this study draws convergence areas related to regional, communication, and learning functions, areas related to problem solving and sensory organs, areas related to art and intelligence, areas related to information and communication, areas related to home and disability, topics, conceptualization, peer-related areas, integration, reorganization, attitudes. In conclusion, this study is meaningful in that it established a framework for designing an activity-centered convergence program of art and language in the future and attempted a holistic approach to support child development.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.169-170
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• 2008

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.511-512
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• 2010

• Annual Conference on Human and Language Technology
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• 2014.10a
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• pp.122-126
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• 2014

감성은 콘텐츠 구매과정에서 결정적인 요소로 작용하며, 영화 콘텐츠의 탐색/소비 과정에서도 콘텐츠 소비의 새로운 기준이다. 그러므로 본 연구에서는 콘텐츠의 내용과 감성을 반영하기 위한 감성분류체계를 제안하였다. 제안한 감성분류체계를 기반으로 사용자의 취향과 감성에 기반하여 콘텐츠를 분류/추천하여 개인화된 편성을 제공하는 것을 "감성 큐레이션"이라 정의하고, 이를 위한 감성기반 큐레이션 방법론을 기술하고 실험을 통해 추천 효과를 입증하였다. 큐레이션은 기존의 개인화 추천과 달리 고객 취향뿐만이 아닌, 신선함, 다양성을 제공할 수 있어야 하며, 상용 큐레이션 서비스에서는 실제 시청으로 연결되는 비율이 중요하다. 본 연구에서는 큐레이션 성능 평가를 위해 성향인지도, 신선도, 다양성에 기반한 만족도 설문조사 방법과 함께, 콘텐츠의 전체 시청률 대비 큐레이션을 통해 추천되어 증가된 시청률의 확대 비율인 Lift score 라는 새로운 평가 방법을 제안하여 그 효용성을 증명하였다.