• Journal of Korean Philosophical Society
• /
• v.129
• /
• pp.267-290
• /
• 2014

The slippery slope argument means that if we accept a type of action A, we are committed to accepting B, C and eventually N. Then, N is situation which we must not accept morally. It works causal mechnism that B because A is raised, C because B is raised. But in the logic textbooks and treatises, the slippery slope argument is classified as fallacy. The reason is that the argument is not a causal argument. Actually, it is a probable. Also it is argued that the argument is wrong because it fears about the future extremely. But We can not say all slippery slope argument is fallacy even though a slippery slope argument is sometimes fallacy. I think it is persuasive argument in a significant place. Therefore I argue that the argument is not simple logic as a form of thinking, but practical reasoning applied the context of dialogue. So in order to find it to be practical reasoning we demand the new understanding to fallacy theory. In traditionally, fallacy is defined to wrong reasoning logically, but according to Walton, fallacy means a verbal tactic or deceptive trick that can be used to cause someone to fall down in argument. That is to say, whether or not the argument is successful depends on how it uses as argument tactic in a given context of dialogue. Therefore I argue that whether or not the argument is successful, because of it is practical problem used in a context of dialogue, is to be approached to pragma and dialectical method, not semantic.

• Journal of the Korea Convergence Society
• /
• v.12 no.12
• /
• pp.93-106
• /
• 2021

Blenderbot 2.0 is a dialogue model representing open domain chatbots by reflecting real-time information and remembering user information for a long time through an internet search module and multi-session. Nevertheless, the model still has many improvements. Therefore, this paper analyzes the limitations and errors of BlenderBot 2.0 from three perspectives: model, data, and dialogue. From the data point of view, we point out errors that the guidelines provided to workers during the crowdsourcing process were not clear, and the process of refining hate speech in the collected data and verifying the accuracy of internet-based information was lacking. Finally, from the viewpoint of dialogue, nine types of problems found during conversation and their causes are thoroughly analyzed. Furthermore, practical improvement methods are proposed for each point of view, and we discuss several potential future research directions.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.5
• /
• pp.213-222
• /
• 2019

Determining the meaning of a keyword in a speech dialogue system is an important technology for the future implementation of an intelligent speech dialogue interface. After extracting keywords to grasp intention from user's utterance, the intention of utterance is determined by using the semantic mark of keyword. One keyword can have several semantic marks, and we regard the task of attaching the correct semantic mark to the user's intentions on these keyword as a problem of word sense disambiguation. In this study, about 23% of all keywords in the corpus is manually tagged to build a semantic mark dictionary, a synonym dictionary, and a context vector dictionary, and then the remaining 77% of all keywords is automatically tagged. The semantic mark of a keyword is determined by calculating the context vector similarity from the context vector dictionary. For an unregistered keyword, the semantic mark of the most similar keyword is attached using a synonym dictionary. We compare the performance of the system with manually constructed training set and semi-automatically expanded training set by selecting 3 high-frequency keywords and 3 low-frequency keywords in the corpus. In experiments, we obtained accuracy of 54.4% with manually constructed training set and 50.0% with semi-automatically expanded training set.

• Proceedings of the Korean Information Science Society Conference
• /
• 2010.11a
• /
• pp.44-45
• /
• 2010

• Proceedings of the Korean Information Science Society Conference
• /
• 2010.06a
• /
• pp.121-122
• /
• 2010

• School Mathematics
• /
• v.1 no.2
• /
• pp.605-615
• /
• 1999

In this paper, we explain the pedagogical phenomena appeared in the learning of 0.$\dot{9}$ = 1 in terms of its intrinsic mathematical structure, and investigate the reason why such phenomena happen. Also we analyze such phenomena through the dialogue between student and teacher, and present some instruction idea from the mathematical and educational view points.

• Korean Architects
• /
• no.4 s.75
• /
• pp.3-8
• /
• 1975

본 논문은 DIALOGUE 1974년 제 3호에 발표된 것을 번역한 것으로 21세기 건축에 큰 공헌을 한 MIES VAN DERROHE와 그의 고층건축에 관하여 설명하고 있다. 저자 '윌리암 조 -디'(WILLIAM H. JORDY)교수는 미국의 각문브라운 저자로는 'AMERICAN BUILDINGS AND THEIR ARCHITECTS'가 있다. 그는 건축계의 여러 잡지에 정기적으로 기고하고 있다.

• Journal of the Korea Convergence Society
• /
• v.8 no.1
• /
• pp.173-179
• /
• 2017

The purpose of this study is to theoretically explore the pedagogical meaning of Flipped Learning (FL) through the lens of Bakhtinnian Dialogism. Flipped learning has emerged based on technological development, which enables educators to reorganize educational space and time. In the new converged time and space, teachers changed the activities in and out of classes, flipped lectures with students' activity, and redefined the role of teachers and students. These dialogical characteristics of FL results in several educational consequences: first, underscoring educational dialogue, second, extending the participation in the dialogical chain, third, deconstructing the existing authority of knowledge, and escalating the self-directedness of learners.

• Annual Conference on Human and Language Technology
• /
• 2017.10a
• /
• pp.111-115
• /
• 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
• /
• 2017.10a
• /
• pp.267-269
• /
• 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.