• Annual Conference on Human and Language Technology
• /
• 2023.10a
• /
• pp.603-608
• /
• 2023

본 연구는 한국어 Vision-Language Pre-training 모델 학습을 위한 대규모 시각-언어 멀티모달 데이터셋 구축에 대한 필요성을 연구한다. 현재, 한국어 시각-언어 멀티모달 데이터셋은 부족하며, 양질의 데이터 획득이 어려운 상황이다. 따라서, 본 연구에서는 기계 번역을 활용하여 외국어(영문) 시각-언어 데이터를 한국어로 번역하고 이를 기반으로 생성형 AI를 활용한 데이터셋 구축 방법론을 제안한다. 우리는 다양한 캡션 생성 방법 중, ChatGPT를 활용하여 자연스럽고 고품질의 한국어 캡션을 자동으로 생성하기 위한 새로운 방법을 제안한다. 이를 통해 기존의 기계 번역 방법보다 더 나은 캡션 품질을 보장할 수 있으며, 여러가지 번역 결과를 앙상블하여 멀티모달 데이터셋을 효과적으로 구축하는데 활용한다. 뿐만 아니라, 본 연구에서는 의미론적 유사도 기반 평가 방식인 캡션 투영 일치도(Caption Projection Consistency) 소개하고, 다양한 번역 시스템 간의 영-한 캡션 투영 성능을 비교하며 이를 평가하는 기준을 제시한다. 최종적으로, 본 연구는 ChatGPT를 이용한 한국어 멀티모달 이미지-텍스트 멀티모달 데이터셋 구축을 위한 새로운 방법론을 제시하며, 대표적인 기계 번역기들보다 우수한 영한 캡션 투영 성능을 증명한다. 이를 통해, 우리의 연구는 부족한 High-Quality 한국어 데이터 셋을 자동으로 대량 구축할 수 있는 방향을 보여주며, 이 방법을 통해 딥러닝 기반 한국어 Vision-Language Pre-training 모델의 성능 향상에 기여할 것으로 기대한다.

• Korean Journal of Radiology
• /
• v.25 no.2
• /
• pp.126-133
• /
• 2024

Large language models (LLMs) have revolutionized the global landscape of technology beyond natural language processing. Owing to their extensive pre-training on vast datasets, contemporary LLMs can handle tasks ranging from general functionalities to domain-specific areas, such as radiology, without additional fine-tuning. General-purpose chatbots based on LLMs can optimize the efficiency of radiologists in terms of their professional work and research endeavors. Importantly, these LLMs are on a trajectory of rapid evolution, wherein challenges such as "hallucination," high training cost, and efficiency issues are addressed, along with the inclusion of multimodal inputs. In this review, we aim to offer conceptual knowledge and actionable guidance to radiologists interested in utilizing LLMs through a succinct overview of the topic and a summary of radiology-specific aspects, from the beginning to potential future directions.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.6
• /
• pp.1692-1705
• /
• 2024

In this paper, we present a method that integrates a Grammar Transducer as an external language model to enhance the accuracy of the pre-trained Korean End-to-end (E2E) Automatic Speech Recognition (ASR) model. The E2E ASR model utilizes the Connectionist Temporal Classification (CTC) loss function to derive hypothesis sentences from input audio. However, this method reveals a limitation inherent in the CTC approach, as it fails to capture language information from transcript data directly. To overcome this limitation, we propose a fusion approach that combines a clause-level n-gram language model, transformed into a Weighted Finite-State Transducer (WFST), with the E2E ASR model. This approach enhances the model's accuracy and allows for domain adaptation using just additional text data, avoiding the need for further intensive training of the extensive pre-trained ASR model. This is particularly advantageous for Korean, characterized as a low-resource language, which confronts a significant challenge due to limited resources of speech data and available ASR models. Initially, we validate the efficacy of training the n-gram model at the clause-level by contrasting its inference accuracy with that of the E2E ASR model when merged with language models trained on smaller lexical units. We then demonstrate that our approach achieves enhanced domain adaptation accuracy compared to Shallow Fusion, a previously devised method for merging an external language model with an E2E ASR model without necessitating additional training.

• English Language & Literature Teaching
• /
• v.17 no.4
• /
• pp.395-411
• /
• 2011

The purpose of this study was to explore the elementary English conversation instructors' perception on their professionalism and the needs of teacher training program. The survey data were attained from 136 elementary English conversation instructors. Descriptive statistics were employed to discuss the result of the survey response. The results of this study revealed that the elementary English conversation instructors perceived the need of in-service training program for their professionalism improvement, especially in teaching techniques for four language skills. The result also revealed that the instructors need to be more equipped with the knowledge of elementary learners' developmental psychology and L2 learning process. The study concludes with several suggestions for elementary English conversation instructors' improvement of teaching professionalism and in-service training program.

• English Language & Literature Teaching
• /
• v.16 no.4
• /
• pp.107-131
• /
• 2010

The goals of the present study are two-fold: 1) to examine NNS teachers' needs for training period in improving their general communicative competence and classroom communicative competence, and 2) to explore the relationships of teachers' needs for the training period, and their current levels of general/classroom communicative competence and other background variables. Data was collected from seventy primary and secondary school English teachers (N=70) who participated in the six-month intensive teacher training program in South Korea. The teacher trainees responded to four questionnaires of 1) the self-diagnosis of their current levels of four language skills (L/S/R/W) in both general/classroom communicative competence, 2) the training period required to improve their general/classroom communicative competence for teaching both English and other subjects through English, 3) the period of their English teaching, and 4) the proportion of their English use in class. The data analysis has shown that there were the strong relationships between trainee needs for the training period and their teaching period, and the proportion of their English use in class. In terms of trainees' communicative competence, the significant relations of both their general/classroom communicative competence and their needs for the training period were found. Implications of the findings are discussed.

• Journal of KIISE:Software and Applications
• /
• v.37 no.3
• /
• pp.205-213
• /
• 2010

We introduce a natural language sentence generation (NLG) method based on learning of word-association patterns. Existing NLG methods assume the inherent grammar rules or use template based method. Contrary to the existing NLG methods, the presented method learns the words-association patterns using only the co-occurrence of words without additional information such as tagging. We employ the hypernetwork method to analyze and represent the words-association patterns. As training going on, the model complexity is increased. After completing each training phase, natural language sentences are generated using the learned hyperedges. The number of grammatically plausible sentences increases after each training phase. We confirm that the proposed method has a potential for learning grammatical properties of training corpuses by comparing the diversity of grammatical rules of training corpuses and the generated sentences.

• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.4
• /
• pp.145-152
• /
• 2020

MRC (Machine reading comprehension) is the AI NLP task that predict the answer for user's query by understanding of the relevant document and which can be used in automated consult services such as chatbots. Recently, the BERT (Pre-training of Deep Bidirectional Transformers for Language Understanding) model, which shows high performance in various fields of natural language processing, have two phases. First phase is Pre-training the big data of each domain. And second phase is fine-tuning the model for solving each NLP tasks as a prediction. In this paper, we have made the Patent MRC dataset and shown that how to build the patent consultation training data for MRC task. And we propose the method to improve the performance of the MRC task using the Pre-trained Patent-BERT model by the patent consultation corpus and the language processing algorithm suitable for the machine learning of the patent counseling data. As a result of experiment, we show that the performance of the method proposed in this paper is improved to answer the patent counseling query.

• Journal of information and communication convergence engineering
• /
• v.18 no.4
• /
• pp.207-215
• /
• 2020

Part of speech (POS) tagging is an indispensable part of major NLP models. Its progress can be perceived on number of languages around the globe especially with respect to European languages. But considering Indian Languages, it has not got a major breakthrough due lack of supporting tools and resources. Particularly for Odia language it has not marked its dominancy yet. With a motive to make the language Odia fit into different NLP operations, this paper makes an attempt to develop a POS tagger for the said language on a HMM (Hidden Markov Model) platform. The tagger judiciously considers bigram HMM with dynamic Viterbi algorithm to give an output annotated text with maximum accuracy. The model is experimented on a corpus belonging to tourism domain accounting to a size of approximately 0.2 million tokens. With the proportion of training and testing as 3:1, the proposed model exhibits satisfactory result irrespective of limited training size.

• Child Health Nursing Research
• /
• v.11 no.4
• /
• pp.444-455
• /
• 2005

Purpose: This study examined the efficacy of parent training interventions to facilitate social reciprocity and language development in children with autism. Methods: The social interaction behaviors of mothers and children over time were compared using single subject design experimentation methodology. five children who were diagnosed with autism and their mothers participated in the study. The participants were recruited from U city, Korea. The mothers were trained using training videotapes and demonstrations on how to facilitate social interaction with their children as well as promoting language development. following the training, data were collected three times per week by video taping mother-child interaction in their homes. Results: Four of the five mothers demonstrated increases in the use of imitation with animation and expectant waiting after the intervention compared to the baseline sessions; the children demonstrated noticeable increases in the use of initiation of interaction, vocalizations, and verbal production after their mothers received the training intervention. Conclusion : Results of this study demonstrate the efficacy of mother training to improve social interactions of children with autism. Additional important information can be gained by replicating this study with more participants and comparing intervention and control groups. Clearly, this intervention shows promise and has implications far clinical practice.

• Phonetics and Speech Sciences
• /
• v.15 no.3
• /
• pp.37-44
• /
• 2023

This study investigates perceptual confusions of English fricatives among 121 Korean elementary school English as a foreign language (EFL) learners with shorter periods of learning English. The objective is to examine how they perceive English fricative consonants and to provide educational guidelines. Two sets of English fricative identification tasks-voiceless fricatives and voiced fricatives-were administered to participants in a High Variability Phonetic Training (HVPT) setting. Their phonemic awareness of the fricatives was visualized in perceptual confusion maps via multidimensional scaling analysis. The findings are explored in terms of the impacts of Korean EFL learners' L1 linguistic aspects and a comparison with L1 learners. Learners' phonemic awareness patterns are then compared with their relative importance in speech intelligibility based on a functional load hierarchy. The results indicated that Korean elementary EFL learners recognized English fricatives in a manner largely akin to L1 learners, suggesting their ongoing acquisition progress. Additionally, the findings demonstrated that the young EFL learners possess sufficient phonemic awareness for most high functional load segments but encounter some difficulties with one high and one low functional pair. The findings of this study offer suggestions for diagnosing language learners' phonemic awareness abilities, thereby aiding in the development of practical guidelines for language instructional design and helping educators make informed decisions regarding teaching priority in L2 classes.