• Journal of The Korean Association For Science Education
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• v.38 no.2
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• pp.97-112
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• 2018

Conducted within the methodological framework of action research, this study examines the ways in which a beginning science teacher in a Korean elementary classroom engaged in collaborative research with her own students to resolve problems preventing effective science teaching and learning. Specifically, this study uses cogenerative dialogue between teachers and students to develop new teachers' knowledge of how to manage the classroom to be able to more effectively implement inquiry instructional strategies and knowledge of students as learners. Findings from this research suggest that by involving students in cogenerative dialogues, beginning teachers are provided with valuable insights into how elementary students think about school, science, and teaching and learning, which can help expand a beginning teacher's capacity to be an effective science teacher of science for all learners, especially diverse learners. These findings suggest that teacher education programs could better support beginning teachers by placing greater emphasis on how to conduct action research, including how to implement cogenerative dialogues to catalyze positive changes in their own classrooms. We conclude by discussing the important implications this research has for supporting new teachers struggle to effectively teach science and who would benefit from using strategies to foster improved relationships with their students and improved understanding about the challenges faced by diverse learners in their classroom.

• Korean Journal of Cognitive Science
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• v.18 no.1
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• pp.1-14
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• 2007

In a goal-oriented dialogue, speakers' intentions can be represented by domain actions that consist of pairs of a speech act and a concept sequence. Therefore, if we plan to implement an intelligent dialogue system, it is very important to correctly infer the domain actions from surface utterances. In this paper, we propose a statistical model to determine speech acts and concept sequences using conditional random fields at the same time. To avoid biased learning problems, the proposed model uses low-level linguistic features such as lexicals and parts-of-speech. Then, it filters out uninformative features using the chi-square statistic. In the experiments in a schedule arrangement domain, the proposed system showed good performances (the precision of 93.0% on speech act classification and the precision of 90.2% on concept sequence classification).

• Korean Journal of Cognitive Science
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• v.18 no.2
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• pp.179-191
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• 2007

In a goal-oriented dialogue, spoken' intentions can be represented by domain actions that consist of pairs of a speech art and a concept sequence. The domain action prediction of user's utterance is useful to correct some errors that occur in a speech recognition process, and the domain action prediction of system's utterance is useful to generate flexible responses. In this paper, we propose a model to predict a domain action of the next utterance using a neural network. The proposed model predicts the next domain action by using a dialogue history vector and a current domain action as inputs of the neural network. In the experiment, the proposed model showed the precision of 80.02% in speech act prediction and the precision of 82.09% in concept sequence prediction.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.267-269
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• 2017

대화 시스템(dialogue system)은 사용자의 언어를 이해하고 그 의도를 분석하여 사용자가 원하는 목적을 달성할 수 있게 도와주는 시스템이다. 인간과 비슷한 수준의 대화를 위해서는 대량의 데이터가 필요하며 데이터의 양질에 따라 그 결과가 달라진다. 최근 페이스북에서 End-to-end learning 방식을 기반으로 한 영어로 구성된 식당 예약 학습 대화 데이터셋(The 6 dialog bAbI tasks)을 구축하여 해당 모델에 적용한 연구가 있다. 대화 시스템에서 활용 가능한 연구가 활발히 진행되고 있지만 영어 기반의 데이터와는 다르게 식당 예약 시스템에서 다른 연구자들의 연구 목적으로 공유한 한국어 데이터셋은 아직까지도 미흡하다. 본 논문에서는 페이스북에서 구축한 영어로 구성된 식당 예약 학습 대화 데이터셋을 이용하여 한국어 기반의 식당 예약 대화 시스템에서 활용 가능한 한국어 데이터셋을 구축하고, 일상생활에서 발생 가능한 발화(utterance)에 따른 형태 변화를 통해 한국어 식당 예약 시스템 데이터셋 구축 방법을 제안한다.

• Korean Journal of Social Welfare
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• v.46
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• pp.236-262
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• 2001

This discovery-oriented study explored 31 social workers' perceptions of discrepancies between explicit and implicit rules in their work places that are supposed to affect the quality of social work services, and identified eight categories of dilemmas: (a) confused accountability or purpose, (b) ambiguous principle, (c) improper authority, (d) confused role of social workers, (e) conflict between ideal and reality, (f) confused work ethics, (g) confused boundary of workers' rights, and (h) binds. These eight categories revealed the real philosophy and purposes of social work agencies, work ethics and values prevalent among social work agencies, agencies' orientation toward clients, and the conditions of social support from the society in large. Instead of searching for discrete variables as separately responsible for inefficient social work services, this approach probed malfunctioning implicit rules in a holistic context to see if inefficient or ineffective provision of social work services is a logical response to a much larger and deeper nexus. Insight into discrepant rules does not solely ensure the improvement of social work practice in the field, particularly if their identification is simply used as another opportunity to blame and avoid self-responsibility. However, such discrepancies between implicit and explicit rules are real enough to the staff workers and agency administrators that they may want to begin the dialogue of contradictory rules as a way of sanctioning discussion of previously forbidden topics. This study provided the ground-work for the dialogue.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.365-368
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• 2005

We propose a speech act analysis method for Korean dialogue using Support Vector Machines (SVM). We use a lexical form of a word, its part of speech (POS) tags, and bigrams of POS tags as sentence features and the contexts of the previous utterance as context features. We select informative features by Chi square statistics. After training SVM with the selected features, SVM classifiers determine the speech act of each utterance. In experiment, we acquired overall $90.54\%$ of accuracy with dialogue corpus for hotel reservation domain.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.111-115
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• 2017

목적 지향적 대화 시스템(Goal-oriented dialogue system) 은 텍스트나 음성을 통해 특정한 목적을 수행 할 수 있는 시스템이다. 최근 RNN(recurrent neural networks)을 기반으로 대화 데이터를 end-to-end learning 방식으로 학습하여 대화 시스템을 구축하는데에 활용한 연구가 있다. End-to-end 방식의 학습은 도메인에 대한 지식 없이 학습 데이터 자체만으로 대화 시스템 구축을 위한 학습이 가능하다는 장점이 있지만 도메인 지식을 학습하기 위해서는 많은 양의 데이터가 필요하다는 단점이 존재한다. 이에 본 논문에서는 도메인 특정 지식을 결합하여 end-to-end learning 방식의 학습이 가능한 Hybrid Code Network 구조를 기반으로 한국어로 구성된 식당 예약에 관련한 대화 데이터셋을 이용하여 식당 예약을 목적으로하는 대화 시스템을 구축하는 방법을 제안한다. 실험 결과 본 시스템은 응답 별 정확도 95%와 대화 별 정확도 63%의 성능을 나타냈다.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.267-269
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• 2017

대화 시스템(dialogue system)은 사용자의 언어를 이해하고 그 의도를 분석하여 사용자가 원하는 목적을 달성할 수 있게 도와주는 시스템이다. 인간과 비슷한 수준의 대화를 위해서는 대량의 데이터가 필요하며 데이터의 양질에 따라 그 결과가 달라진다. 최근 페이스북에서 End-to-end learning 방식을 기반으로 한 영어로 구성된 식당 예약 학습 대화 데이터셋(The 6 dialog bAbI tasks)을 구축하여 해당 모델에 적용한 연구가 있다. 대화 시스템에서 활용 가능한 연구가 활발히 진행되고 있지만 영어 기반의 데이터와는 다르게 식당 예약 시스템에서 다른 연구자들의 연구 목적으로 공유한 한국어 데이터셋은 아직까지도 미흡하다. 본 논문에서는 페이스북에서 구축한 영어로 구성된 식당 예약 학습 대화 데이터셋을 이용하여 한국어 기반의 식당 예약 대화 시스템에서 활용 가능한 한국어 데이터셋을 구축하고, 일상생활에서 발생 가능한 발화(utterance)에 따른 형태 변화를 통해 한국어 식당 예약 시스템 데이터셋 구축 방법을 제안한다.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.1
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• pp.89-98
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• 2004

Conversational agent is a system that provides users with proper information and maintains the context of dialogue on the natural language. Analyzing and modeling process of user's query is essential to make it more realistic, for which Bayesian network is a promising technique. When experts design the network for a domain, the network is usually very complicated and is hard to be understood. The separation of variables in the domain reduces the size of networks and makes it easy to design the conversational agent. Composing Bayesian network as two stages, we aim to design conversational agent easily and analyze user's query in detail. Also, previous information of dialogue makes it possible to maintain the context of conversation. Actually implementing it for a guide of web pages, we can confirm the usefulness of the proposed architecture for conversational agent.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1777-1778
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• 2007

In this paper, We design and implement a humanoid robot, With Educational purpose, which can collaborate and communicate with human. We present an affective human-robot communication system for a humanoid robot, D2, which we designed to communicate with a human through dialogue. D2 communicates with humans by understanding and expressing emotion using facial expressions, voice, gestures and posture. Interaction between a human and a robot is made possible through our affective communication framework. The framework enables a robot to catch the emotional status of the user and to respond appropriately. As a result, the robot can engage in a natural dialogue with a human. According to the aim to be interacted with a human for voice, gestures and posture, the developed Educational humanoid robot consists of upper body, two arms, wheeled mobile platform and control hardware including vision and speech capability and various control boards such as motion control boards, signal processing board proceeding several types of sensors. Using the Educational humanoid robot D2, we have presented the successful demonstrations which consist of manipulation task with two arms, tracking objects using the vision system, and communication with human by the emotional interface, the synthesized speeches, and the recognition of speech commands.