• Title/Summary/Keyword: Language task

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Performance of Vocabulary-Independent Speech Recognizers with Speaker Adaptation

  • Kwon, Oh Wook;Un, Chong Kwan;Kim, Hoi Rin
    • The Journal of the Acoustical Society of Korea
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    • v.16 no.1E
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    • pp.57-63
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    • 1997
  • In this paper, we investigated performance of a vocabulary-independent speech recognizer with speaker adaptation. The vocabulary-independent speech recognizer does not require task-oriented speech databases to estimate HMM parameters, but adapts the parameters recursively by using input speech and recognition results. The recognizer has the advantage that it relieves efforts to record the speech databases and can be easily adapted to a new task and a new speaker with different recognition vocabulary without losing recognition accuracies. Experimental results showed that the vocabulary-independent speech recognizer with supervised offline speaker adaptation reduced 40% of recognition errors when 80 words from the same vocabulary as test data were used as adaptation data. The recognizer with unsupervised online speaker adaptation reduced abut 43% of recognition errors. This performance is comparable to that of a speaker-independent speech recognizer trained by a task-oriented speech database.

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Utilization of Vision in Off-Line Teaching for assembly robot (조립용 로봇의 오프라인 교시를 위한 영상 정보의 이용에 관한 연구)

  • 안철기
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2000.04a
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    • pp.543-548
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    • 2000
  • In this study, an interactive programming method for robot in electronic part assembly task is proposed. Many of industrial robots are still taught and programmed by a teach pendant. The robot is guided by a human operator to the desired application locations. These motions are recorded and are later edited, within the robotic language using in the robot controller, and play back repetitively to perform robot task. This conventional teaching method is time-consuming and somewhat dangerous. In the proposed method, the operator teaches the desired locations on the image acquired through CCD camera mounted on the robot hand. The robotic language program is automatically generated and downloaded to the robot controller. This teaching process is implemented through an off-line programming software. The OLP is developed for an robotic assembly system used in this study. In order to transform the location on image coordinates into robot coordinates, a calibration process is established. The proposed teaching method is implemented and evaluated on an assembly system for soldering electronic parts on a circuit board. A six-axis articulated robot executes assembly task according to the off-line teaching in the system.

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A Study on the Development of an Automatic Robot Programming System (로보트 자동 프로그래밍 시스템 개발에 관한 연구)

  • 조혜경;이범희;고명삼
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.38 no.9
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    • pp.740-752
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    • 1989
  • Many works have been reported in various fields on the subject of controlling a robot with high-level robot languages. This paper presents one such effort and explains the development of an automatic robot programming system which utilizes the concept of the task level language. This system is expected to act as an intelligent supporting tool in robot programming and be put into practical use. Emphasis is placed on the role of the programming system as a tool that generates the executable robot program according to the user specified tasks. Several task level commands are used in the developed system, and the resulting inflexibility is complemented by the motion level commands of the motion level robot languages. Thus, the advantages of both task and motion level languages are utilized, and no knowledge of specific language grammer is needed even when using motion level commands. To increase the usability of the developed system, various methods are provided for supplementing the programming system using taught data.

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Recent R&D Trends for Pretrained Language Model (딥러닝 사전학습 언어모델 기술 동향)

  • Lim, J.H.;Kim, H.K.;Kim, Y.K.
    • Electronics and Telecommunications Trends
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    • v.35 no.3
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    • pp.9-19
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    • 2020
  • Recently, a technique for applying a deep learning language model pretrained from a large corpus to fine-tuning for each application task has been widely used as a language processing technology. The pretrained language model shows higher performance and satisfactory generalization performance than existing methods. This paper introduces the major research trends related to deep learning pretrained language models in the field of language processing. We describe in detail the motivations, models, learning methods, and results of the BERT language model that had significant influence on subsequent studies. Subsequently, we introduce the results of language model studies after BERT, focusing on SpanBERT, RoBERTa, ALBERT, BART, and ELECTRA. Finally, we introduce the KorBERT pretrained language model, which shows satisfactory performance in Korean language. In addition, we introduce techniques on how to apply the pretrained language model to Korean (agglutinative) language, which consists of a combination of content and functional morphemes, unlike English (refractive) language whose endings change depending on the application.

Comparative study of text representation and learning for Persian named entity recognition

  • Pour, Mohammad Mahdi Abdollah;Momtazi, Saeedeh
    • ETRI Journal
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    • v.44 no.5
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    • pp.794-804
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    • 2022
  • Transformer models have had a great impact on natural language processing (NLP) in recent years by realizing outstanding and efficient contextualized language models. Recent studies have used transformer-based language models for various NLP tasks, including Persian named entity recognition (NER). However, in complex tasks, for example, NER, it is difficult to determine which contextualized embedding will produce the best representation for the tasks. Considering the lack of comparative studies to investigate the use of different contextualized pretrained models with sequence modeling classifiers, we conducted a comparative study about using different classifiers and embedding models. In this paper, we use different transformer-based language models tuned with different classifiers, and we evaluate these models on the Persian NER task. We perform a comparative analysis to assess the impact of text representation and text classification methods on Persian NER performance. We train and evaluate the models on three different Persian NER datasets, that is, MoNa, Peyma, and Arman. Experimental results demonstrate that XLM-R with a linear layer and conditional random field (CRF) layer exhibited the best performance. This model achieved phrase-based F-measures of 70.04, 86.37, and 79.25 and word-based F scores of 78, 84.02, and 89.73 on the MoNa, Peyma, and Arman datasets, respectively. These results represent state-of-the-art performance on the Persian NER task.

Correlation between Pragmatic Language and Executive Function in Patients with Acquired Brain Injury (후천성 뇌손상 환자의 화용언어와 집행기능 간 상관성)

  • Lee, Mi-Sook
    • The Journal of the Korea Contents Association
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    • v.16 no.5
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    • pp.58-67
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    • 2016
  • Pragmatic language impairment is closely related to the executive function difficulties in patients with acquired brain injury(ABI). This study was designed to explore the correlation between two domains following ABI. Thirty-five participants with ABI were grouped into 21 aphasics due to stroke and 14 TBIs. All subjects were over 55 years old. Measures of two domains were administered to all participants. As a result, figurative language comprehension and functional/symbolic language were significantly correlated with the activating task in aphasic group. All tasks were significantly correlated in TBI group. Aphasic patients' figurative language comprehension significantly predicted the activating task. In TBI group, figurative language expression and functional/symbolic language were the predictable tasks of planning and activating, respectively. Current study demonstrates the evidence of a significant association between pragmatic language and executive function, and provides appropriate tasks used for cognitive-linguistic intervention of individuals with ABI.

A Comparative Study of Peer-driven and Task-driven on Reading Training

  • Luo, Derong
    • International Journal of Advanced Culture Technology
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    • v.8 no.2
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    • pp.101-108
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    • 2020
  • One difficulty in language learning is the training of reading ability. The improvement on this ability directly affects the process and effect of language learning. At the same time, there are numerous difficulties in actual learning and teaching. Depending on current research, there is two ideas that can utilize to enhance the reading efficiency of learners. One is to amend objective factors; the other is to change subjective factors. Compared with the two ideas, idiosyncratic factors are more manipulable and controllable, so it is more valuable to conduct researches on this. But among the many subjective factors, the degree of their effectiveness is not the same, so this article attempts to compare and analyze the driving effects of two important subjective factors (peer-driven and task-driven) on reading performance. The results show that both factors can have a positive impact on reading comprehension, but different in driving effects. The task-driven has obvious short-term effectiveness; while peer-driven needs to establish its long-term effect on the basis of early coordination and cooperation among team members. Therefore, in order to maximize the achievement of learning, it is necessary to combine strengths and avoid weaknesses according to the characteristics of two factors, so as to help learners improve reading ability most efficiently.

Cross-Lingual Style-Based Title Generation Using Multiple Adapters (다중 어댑터를 이용한 교차 언어 및 스타일 기반의 제목 생성)

  • Yo-Han Park;Yong-Seok Choi;Kong Joo Lee
    • KIPS Transactions on Software and Data Engineering
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    • v.12 no.8
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    • pp.341-354
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    • 2023
  • The title of a document is the brief summarization of the document. Readers can easily understand a document if we provide them with its title in their preferred styles and the languages. In this research, we propose a cross-lingual and style-based title generation model using multiple adapters. To train the model, we need a parallel corpus in several languages with different styles. It is quite difficult to construct this kind of parallel corpus; however, a monolingual title generation corpus of the same style can be built easily. Therefore, we apply a zero-shot strategy to generate a title in a different language and with a different style for an input document. A baseline model is Transformer consisting of an encoder and a decoder, pre-trained by several languages. The model is then equipped with multiple adapters for translation, languages, and styles. After the model learns a translation task from parallel corpus, it learns a title generation task from monolingual title generation corpus. When training the model with a task, we only activate an adapter that corresponds to the task. When generating a cross-lingual and style-based title, we only activate adapters that correspond to a target language and a target style. An experimental result shows that our proposed model is only as good as a pipeline model that first translates into a target language and then generates a title. There have been significant changes in natural language generation due to the emergence of large-scale language models. However, research to improve the performance of natural language generation using limited resources and limited data needs to continue. In this regard, this study seeks to explore the significance of such research.

Deep recurrent neural networks with word embeddings for Urdu named entity recognition

  • Khan, Wahab;Daud, Ali;Alotaibi, Fahd;Aljohani, Naif;Arafat, Sachi
    • ETRI Journal
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    • v.42 no.1
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    • pp.90-100
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    • 2020
  • Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state-of-the-art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short-term memory and back propagation through time approaches. The proposed models consider both language-dependent features, such as part-of-speech tags, and language-independent features, such as the "context windows" of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f-measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.

Disfluency Characteristics in 4-6 Age Bilingual Children (4-6세 이중언어아동의 비유창성 특성 연구)

  • Lee, Soo-Bok;Sim, Hyun-Sub;Shin, Moon-Ja
    • Proceedings of the KSPS conference
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    • 2007.05a
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    • pp.78-83
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    • 2007
  • The purpose of present study was to investigate the characteristics of disfluency between the Korean-English bilingual and Korean monolingual children, matched by their chronological age with the bilingual children. Twenty-eight children, 14 bilingual children and 14 monolingual children participated in this study. The experimental tasks consisted of the play situation and the task situation. The conclusion is (a) The score of total disfluency of the bilingual was significantly higher than that of the monolingual. The score of normal disfluency of the bilingual was significantly higher than that of the monolingual. The most frequent type is Interjection in both groups. All shows higher score in the task situation than the play situation. The bilingual children have quantitative and qualitative differences in disfluency score and types from the monolingual. (b) The bilingual were divided into two groups such as 6 Korean-dominant bilingual and 8 English-dominant bilingual. All shows more disfluency in their non-dominant language. The most frequent type is Interjection in both groups. (c) The higher the chronological age and the expressive language test score is, the lower the disfluency score is. The earlier the exposure age to the 2nd language is, the higher the disfluency score is. There is no correlation between resident month at foreign country and the disfluency.

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