• Educational Technology International
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• v.10 no.2
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• pp.145-166
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• 2009

The purpose of this study was to investigate why advance organizers (AO) are effective in promoting comprehension and mental model formation in terms of cognitive load. Two experimental groups: a concept-map AO group and a key-word AO group and one control group were used. This study considered cognitive load in view of Baddeley's working memory model: central executive (CE), phonological loop (PL), and visuo-spatial sketch pad (VSSP). The present experiment directly examined cognitive load using dual task methodology. The results were as follows: central executive (CE) suppression task achievement for the concept map AO group was higher than the key word AO group and control group. Comprehension and mental model construction for the concept map AO group were higher than the other groups. These results indicated that the superiority of concept map AO owing to CE load decrement occurred with comprehension and mental model construction in learning. Thus, the available resources produced by CE load reduction may have been invested for comprehension and mental model construction of learning contents.

• ETRI Journal
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• v.41 no.3
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• pp.371-382
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• 2019

Machine reading comprehension is the task of understanding a given context and finding the correct response in that context. A simple recurrent unit (SRU) is a model that solves the vanishing gradient problem in a recurrent neural network (RNN) using a neural gate, such as a gated recurrent unit (GRU) and long short-term memory (LSTM); moreover, it removes the previous hidden state from the input gate to improve the speed compared to GRU and LSTM. A self-matching network, used in R-Net, can have a similar effect to coreference resolution because the self-matching network can obtain context information of a similar meaning by calculating the attention weight for its own RNN sequence. In this paper, we construct a dataset for Korean machine reading comprehension and propose an $S^2-Net$ model that adds a self-matching layer to an encoder RNN using multilayer SRU. The experimental results show that the proposed $S^2-Net$ model has performance of single 68.82% EM and 81.25% F1, and ensemble 70.81% EM, 82.48% F1 in the Korean machine reading comprehension test dataset, and has single 71.30% EM and 80.37% F1 and ensemble 73.29% EM and 81.54% F1 performance in the SQuAD dev dataset.

• ETRI Journal
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• v.41 no.6
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• pp.771-781
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• 2019

We propose the VS³-NET model to solve the task of question answering questions with machine-reading comprehension that searches for an appropriate answer in a given context. VS³-NET is a model that trains latent variables for each question using variational inferences based on a model of a simple recurrent unit-based sentences and self-matching networks. The types of questions vary, and the answers depend on the type of question. To perform efficient inference and learning, we introduce neural question-type models to approximate the prior and posterior distributions of the latent variables, and we use these approximated distributions to optimize a reparameterized variational lower bound. The context given in machine-reading comprehension usually comprises several sentences, leading to performance degradation caused by context length. Therefore, we model a hierarchical structure using sentence encoding, in which as the context becomes longer, the performance degrades. Experimental results show that the proposed VS³-NET model has an exact-match score of 76.8% and an F1 score of 84.5% on the SQuAD test set.

• English Language & Literature Teaching
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• v.15 no.4
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• pp.345-364
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• 2009

This study attempts to provide a comprehensive framework for listening strategy use among university students in Korea in relation to TOEIC listening scores. In particular, this study tests whether motivation, based on the self-determination theory, mediates listening strategy use on listening comprehension (LC) process and whether reading comprehension ability moderates the use of listening strategy in LC achievement. One hundred seventy six freshmen students participated in the study during their first semester required English course. Self-report questionnaires were used to assess students' motivation and LC strategy use. The responses were statistically analyzed via the moderator and mediator model. The results indicate that internalized motivation mediates the use of listening strategy in LC achievement; however, reading comprehension skill doesn't affect students' use of listening strategies in relation to listening skill achievement. In other words, students who have internalized motivation were able to utilize listening strategies effectively in terms of achievement of the TOEIC listening skills. The findings of the current study offer in-depth understanding of the relationship among use of LC strategies, intrinsic motivation, and listening skill achievement shared by the mediator and moderator models.

• Speech Sciences
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• v.8 no.4
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• pp.185-191
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• 2001

This study was designed to determine what components of spoken language have been relatively neglected in the teaching of listening comprehension in Korea and to suggest a model of listening process. Two types of tests were undertaken using spoken and written forms of English with secondary school teachers of English and college students. Findings: Hearing power has been generally neglected in the teaching of listening comprehension. Hearing power which can be thought as an active process is defined as an ability to transfer the sequence of discrete phonetic segments without word boundary into the sequence of words in phonemic representations by using both nonlinguistic factors and linguistic factors including perception rules based on phonetics and phonology. Vocabularies, hearing-speaking power, syntactic structures and idiomatic expressions are to be taught for spoken English. A model of listening process was suggested and discussed.

• Speech Sciences
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• v.10 no.4
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• pp.241-254
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• 2003

The purpose of the present study was to investigate the sentence comprehension strategies used by Korean/English bilingual children when they listened to sentences of their first language, i.e., Korean. The framework of competition model was employed to analyze the influence of the second language, i.e., English, during comprehension of Korean sentences. The participants included 10 bilingual children (ages 7;4-13;0) and 20 Korean-speaking monolingual children(ages 5;7-6;10) with similar levels of development in Korean language as bilingual children. In an act-out procedure, the children were asked to determine the agent in sentences composed of two nouns and a verb with varying conditions of three cues (case-marker, animacy, and word-order). The results revealed that both groups of children used the case marker cues as the strongest cue among the three. The bilingual children relied on case-marker cues even more than the monolingual children. However, the bilingual children used animacy cues significantly less than the monolingual children. There were no significant differences between the groups in the use of word-order cues. The bilingual children appeared less effective in utilizing animacy cues in Korean sentence comprehension due to the backward transfer from English where the cue strength of animacy is very weak. The influence of the second language on the development of the first language in bilingual children was discussed.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.2
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• pp.69-76
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• 2018

Referring expressions are natural language constructions used to identify particular objects within a scene. In this paper, we propose a new deep neural network model for referring expression comprehension. The proposed model finds out the region of the referred object in the given image by making use of the rich information about the referred object itself, the context object, and the relationship with the context object mentioned in the referring expression. In the proposed model, the object matching score and the relationship matching score are combined to compute the fitness score of each candidate region according to the structure of the referring expression sentence. Therefore, the proposed model consists of four different sub-networks: Language Representation Network(LRN), Object Matching Network (OMN), Relationship Matching Network(RMN), and Weighted Composition Network(WCN). We demonstrate that our model achieves state-of-the-art results for comprehension on three referring expression datasets.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.351-360
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• 2020

If various unreasonable safety standards are preemptively and effectively readjusted, the risk of accidents can be reduced. In this paper, we proposed a machine reading comprehension-based safety standard Q&A system to secure supporting technology for effective search and analysis of safety standards for integrated and systematic management of safety standards. The proposed model finds documents related to safety standard questions in the various laws and regulations, and then divides these documents into provisions. Only those provisions that are likely to contain the answer to the question are selected, and then the BERT-based machine reading comprehension model is used to find answers to questions related to safety standards. When the proposed safety standard Q&A system is applied to KorQuAD dataset, the performance of EM 40.42% and F1 55.34% are shown.

• Korean Journal of Cognitive Science
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• v.14 no.3
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• pp.23-36
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• 2003

A comprehension-based approach to learning assumes that incoming information and background knowledge are integrated to form a mental representation which is subsequently used to incorporate new knowledge. It is demonstrated that this approach can be validated by comparing human and computational model performance in the prompt learning context. A computational model (ADAPT-UNIX) based on the construction-integration theory of comprehension (Kintsch, 1988; 1998) predicted how users learn from help prompts which are designed to assist UNIX composite command production. In addition, the comparison also revealed high similarity in composite production task performance between model and human. Educational implications of present research are discussed on the basis of the fact that prompt instructions have differential effect on learning and application as background knowledge varies.

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