• Speech Sciences
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• v.7 no.3
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• pp.249-262
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• 2000

The purpose of the present study was to investigate the effect of syntactic complexity (sentence length and sentence structure) on sentence repetition performance and intelligibility between specific language impairment (SLI) and normal children. Thirteen SLI children and twenty-six normal children, matched by 3 years of language, participated in this study. The sentence repetition performance of the subjects were analyzed based on the sentence length (3-word simple sentences and 5-word simple sentences) and sentence structure (5-word simple sentences, 5-word conjoined complex sentences, and 5-word embedded complex sentences). The results of this study indicated the sentence structure influenced sentence repetition performance and intelligibility of SLI children only. The implication of these findings were discussed.

• Journal of Korean Library and Information Science Society
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• v.36 no.2
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• pp.49-68
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• 2005

The purpose of this study is to examine the research trends and their experiment results on the applications of the language models for information retrieval. We reviewed the previous studies with the following categories: (1) the first generation of language modeling information retrieval (LMIR) experiments which are mainly focused on comparing the language modeling information retrieval with the traditional retrieval models in their retrieval performance, and (2) the second generation of LMIR experiments which are focused on comparing the expanded language modeling information retrieval with the basic language models in their retrieval performance. Through the analysis of the previous experiments results, we found that (1) language models are outperformed the probabilistic model or vector space model approaches, and (2) the expended language models demonstrated better results than the basic language models in their retrieval performance.

• Clinical and Experimental Pediatrics
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• v.51 no.9
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• pp.922-934
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• 2008

Developmental language disorder is the most common developmental disability in childhood, occurring in 5-8% of preschool children. Children learn language in early childhood, and later they use language to learn. Children with language disorders are at increased risk for difficulties with reading and written language when they enter school. These problems often persist through adolescence or adulthood. Early intervention may prevent the more serious consequences of later academic problems, including learning disabilities. A child's performance in specific speech and language areas, such as phonological ability, vocabulary comprehension, and grammatical usage, is measured objectively using the most recently standardized, norm-referenced tests for a particular age group. Observation and qualitative analysis of a child's performance supplement objective test results are essential for making a diagnosis and devising a treatment plan. Emphasis on the team approach system in the evaluation of children with speech and language impairments has been increasing. Evidence-based therapeutic interventions with short-term, long-term, and functional outcome goals should be applied, because there are many examples of controversial practices that have not been validated in large, controlled trials. Following treatment intervention, periodic follow-up monitoring by a doctor is also important. In addition, a systematized national health policy for children with speech and language disorders should be provided.

• Smart Media Journal
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• v.12 no.6
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• pp.35-40
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• 2023

In this study paper, we examine pre-trained language models used in Multi-Goal Conversational Recommender Systems (MG-CRS), comparing and analyzing their performances of various pre-trained language models. Specifically, we investigates the impact of the sizes of language models on the performance of MG-CRS. The study targets three types of language models - of BERT, GPT2, and BART, and measures and compares their accuracy in two tasks of 'type prediction' and 'topic prediction' on the MG-CRS dataset, DuRecDial 2.0. Experimental results show that all models demonstrated excellent performance in the type prediction task, but there were notable provide significant performance differences in performance depending on among the models or based on their sizes in the topic prediction task. Based on these findings, the study provides directions for improving the performance of MG-CRS.

• Journal of Korean language education
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• v.29 no.3
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• pp.227-253
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• 2018

The purpose of this study is to propose a plan for future developments of the Korean language textbooks for overseas by conducting the Importance-Performance Analysis (IPA) of the Korean language textbooks for overseas. For this purpose, this study analyse and evaluate the Korean language textbooks for overseas and the researches for developing Korean language textbooks for overseas. In this study, we have the IPA of the Korean language textbooks from the total of 158 surveys that were collected from teachers who teach Korean at King Sejong Institute and overseas university. The survey conducted about the Korean textbooks regarding the following questionnaires: 1) integrated and separated textbooks, 2) textbooks by learners' variables, 3) teaching materials by media type, 4) supplementary teaching materials, 5) diffusion and support of textbooks. The result of this survey found that supporting for the separated textbooks is needed, and there is a high demand for localized textbooks considering local characteristics. Furthermore, it is noteworthy that King Sejong Institute has a high demand for textbooks that can be downloaded from the web despite most of institutes are highly satisfied with paper textbooks. For the supplementary textbooks, it was found that vocabulary learning materials were needed for the King Sejong school students and additional reading materials for overseas college learners needed to be developed. We also found that it is necessary to support not only the development of textbooks but also smooth and efficient diffusion.

• MALSORI
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• no.51
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• pp.57-69
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• 2004

Comprehension monitoring is a process of message evaluation and a very important skill of communication. Comprehension monitoring is a necessary language skill in classroom, as it is important for children to assess their own understanding of task instructions and teaching content. The present study investigated comprehension monitoring skill of children with Specific Language Impairment(SLI) compared to age-matched children and language-matched children. 18 vignettes and 6 displays were constructed. Children were asked to choose one object or 'DK' card from a display after the children listened to the vignettes. The results showed that children with SLI didn't have problem in comprehending unambiguous utterance and using prior statement. However, they had problem in monitoring comprehending ambiguous utterance compared age-matched children. Their performance were similar to language matched younger children. With respect to their performance, several possible explanations were discussed.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.131-134
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• 2007

We propose a multi-stage recognizer architecture that reduces the computation load and makes fast recognizer. To improve performance of baseline multi-stage recognizer, we introduced new feature. We used confidence vector for each phone segment instead of best phoneme sequence. The multi-stage recognizer with new feature has better performance on n-best and has more robustness.

• Korean Journal of Child Studies
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• v.31 no.1
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• pp.65-82
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• 2010

This study investigated the longitudinal effects of family risk factors, parent-child relationships, and language abilities of children in low-income families in terms of both school adjustment and academic performance. The subjects were 176 children aged 5 to 7 and their mothers. They participated in follow up studies over the next 3 years. The children were tested using the Wechsler Intelligence Scales and language tests; and classroom teachers rated their levels of both school adjustment and academic performance. Mothers reported parent-child relationships, maternal depression, and family economic resources. Data were analyzed using Pearson's correlation, and structural equation modeling (SEM). Our results indicated that there were direct effects of language abilities, and indirect effects of parent-child relationships and maternal depression upon children's school adjustment and academic performance. It was also revealed that language abilities had a mediating effect between parent-child relationship and school adjustment/ academic performance.

• Lingua Humanitatis
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• v.5
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• pp.221-264
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• 2003

This paper takes issue with the idea of language as a 'serial-time structure' as opposed to the 'real-time event' of speech, an idea entrenched in Chomskyan model of linguistic theory. The discussion centers around the leitmotif question: Is language constructed entirely from a finite set of apriori discrete symbol types, as the 'competence vs performance' dichotomy implies\ulcorner A set of linguistic patterns examined in this study, largely with regard to phonological considerations, points to the evidence to the contrary. That is, while the patterns may be said to be linguistically distinct, they are not discretely, different, i.e. not different enough to be reliably differentiated. It is demonstrated that much of current research in phonology, including the most recent Optimality Theory, is misdirected in that it falsely presupposes a discrete universal phonetic inventory. The main thrust of the present study is that there is no sharp boundary between 'competence' defined as the formal, symbolic, discrete time domain of language and human cognition on the one hand and 'performance' as the continuous, fuzzy, real-time domain of human physiology on the other.

• Journal of Information Technology Services
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• v.19 no.5
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• pp.83-91
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• 2020

Language Models such as BERT has been an important factor of deep learning-based natural language processing. Pre-training the transformer-based language models would be computationally expensive since they are consist of deep and broad architecture and layers using an attention mechanism and also require huge amount of data to train. Hence, it became mandatory to do fine-tuning large pre-trained language models which are trained by Google or some companies can afford the resources and cost. There are various techniques for fine tuning the language models and this paper examines three techniques, which are data augmentation, tuning the hyper paramters and partly re-constructing the neural networks. For data augmentation, we use no-answer augmentation and back-translation method. Also, some useful combinations of hyper parameters are observed by conducting a number of experiments. Finally, we have GRU, LSTM networks to boost our model performance with adding those networks to BERT pre-trained model. We do fine-tuning the pre-trained korean-based language model through the methods mentioned above and push the F1 score from baseline up to 89.66. Moreover, some failure attempts give us important lessons and tell us the further direction in a good way.