• Korean Journal of Cognitive Science
• /
• v.10 no.2
• /
• pp.27-34
• /
• 1999

The main purpose of this paper is to propose a general dialogue flow in 'the a appointment scheduling dialogues' in German using the concept of dialogue acts. A basic a assumption of this research is that dialogue acts contribute to the improvement of a translation system. They might be very useful to solve the problems that syntactic and semantic module could not resolve using contextual knowledge. The classification of the dialogue acts was conducted as a work of VERBMOBIL project and was based on real dialogues transcribed by experts. The real dialogues were analyzed in terms of the dialogue acts. We empirically analyzed the sequences of the dialogue acts not only in a series of dialogue turns but also in one dialogue turn. We attempted to analyZe the sequences in one dialogue turn additionally because the dialogue data used in this research showed some difference from the ones in other existing researches. By examining the sequences in dialogue acts. we proposed the dialogue flowchart in 'the a appointment scheduling dialogues' 'Based on the statistical analysis of the sequences of the most frequent dialogue acts. the dialogue flowcharts seem to represent' the a appointment scheduling dialogues' in general. A further research is required on c classification of dialogue acts which was a base for the analysis of dialogues. In order to e extract the most generalized model. we did not subcategorize each dialogue acts and used a limited number of items of dialogue acts. However. generally defined dialogue acts need to be defined more concretely and new dialogue acts for specific situations should be a added.

• Journal of Korean Academy of Nursing
• /
• v.39 no.2
• /
• pp.166-176
• /
• 2009

Purpose: The purpose of the study was to identify functional structure and patterns of dialogue sequence in conversations between elderly patients with dementia and nurses in a long-term care facility. Methods: Conversation analysis was used to analyze the data which were collected using video-camera to capture non-verbal as well as verbal behaviors. Data collection was done during February 2005. Results: Introduction, assessment, intervention, and closing phases were identified as functional structure. Essential parts of the conversation were the assessment and intervention phases. In the assessment phase three sequential patterns of nurse-initiated dialogue and four sequential patterns of patient-initiated dialogue were identified. Also four sequential patterns were identified in nurse-initiated and three in patient-initiated dialogues in the intervention phase. In general, "ask question", "advise", and "directive" were the most frequently used utterance by nurses in nurse-initiated dialogue, indicating nurses' domination of the conversation. At the same time, "ask back", "refute", "escape", or "false promise" were used often by nurses to discourage patients from talking when patients were raising questions or demanding. Conclusion: It is important for nurses to encourage patient-initiated dialogue to counterbalance nurse-dominated conversation which results from imbalance between nurses and patients in terms of knowledge and task in healthcare institutions for elders.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.34C no.5
• /
• pp.55-65
• /
• 1997

In many cases, a dialogue system can't extract the correct analysis information of a user's spoken utterance, because of its own ungrammatical components. Therefore, in order to perform a correct before it performs the syntactic processing. In this paper, we use a real dialogue corpus and classify these ungrammatical errors as 4 categories : phonological, syntactic, semantic errors that consist of speech reparis and inversions, and propose an algorithm to detect and correct the errors. In short, this paper proposes a method to detect and correct the speech repairs and inversions that are classified as the phonological and syntactic errors to implement a robust dialogue system. And, through the test of real dialogue data, this paper shows an efficiency of the proposed algorithm.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.10
• /
• pp.98-106
• /
• 1996

This paper proposes an algorithm for analyzing illocutionary force type (IfT)s in a dialogue, based on the context and modal information in the ending of a word. In korean, the variation of an illocutionary force type that represents a speaker's intention frequently occurs at the ending of a word, according to the type of modal information. And in an analysis of speech acts, the modal information illocutionary force types. In this paper, we analyze real dialogue dta, classify the types of illocutionary forces, perform the manual tagging of IFTs and show the freqency of each IFT's occurence. And we also propose an algorithm to extract IFTs, based on the relationship between the analyzed IFTs and the endings of a word. And we use this proposed algorithm to make an experiment on dialogue data and show its efficiency.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.15 no.4 s.40
• /
• pp.453-461
• /
• 1991

The purpose of this research was to develop an automatic design with dialogue function in computer for pants pattern making. AutoCAD including AutoLISP was the programable software package, so AutoCAD were used basically for this research. The conclusions were like these; 1. Dialogue functions of computer allowed the designer to choose emotional elements. 2. The coordinate points needed in drafting for women's pants pattern making were suggest-ed by numerical fomula. So, if the input data of body sizes needed were used, pants patterns for person were automatically obtained as the output. 3. The several curvature parts were presented by using exponent function and the arc drawing of AutoCAD and the degree of bends were to be selected by choosing the simple parameter of algebraic function and arc AutoCAD command. 4. The program permited pattern manipulation and pattern grading of five standard sizes were presented. Also its flow chart by AutoLISP with dialogue function were presented.

• Journal of The Korean Association of Information Education
• /
• v.24 no.6
• /
• pp.623-631
• /
• 2020

In the era of COVID-19 global pandemic, e-learning has become new standards and daily life in the name of 'new normal'. This study developed dialogue e-learning contents as opposed to monologue e-learning which is unidirectional and instructor centered and conducted qualitative user experience evaluation of dialogue e-learning contents. A total number of 20 adult students participated and were individually interviewed. Qualitative data analysis was performed. The findings include students' positive perceptions of dialogue e-learning contents such as empathy for various ideas and new format. With regard to personal preference, 55% of participants preferred dialogue e-learning contents because it enables them to focus and share real experiences. Meanwhile, in terms of learning effects, 60% participants selected monologue e-learning contents and mentioned adequate explanations of concepts and explicit information delivery. Based on the results, suggestions on the design and development of dialogue e-learning contents were presented.

• KIPS Transactions on Software and Data Engineering
• /
• v.6 no.11
• /
• pp.499-506
• /
• 2017

Chatter bots are normally used in task-oriented dialogue systems to support free conversations. However, there is not much research on how chatter bots as auxiliary system should be different from independent ones. In this paper, we have developed a chatter bot for a dialogue-based computer assisted language learning (DB-CALL) system. We compared the chatter bot in two different cases: as an independent bot, and as an auxiliary system. The results showed that, the chatter bot as an auxiliary system showed much lower satisfaction than the independent one. A discussion is held about the difference between an auxiliary chatter bot and an independent bot. In addition, we evaluated a search-based chatter bot and a deep learning based chatter bot. The advantages and disadvantages of both methods are discussed.

• Journal of Broadcast Engineering
• /
• v.24 no.1
• /
• pp.48-57
• /
• 2019

The purpose of this study is to understand the intention of the inquirer from the single text type question in Goal-oriented dialogue. Goal-Oriented Dialogue system means a dialogue system that satisfies the user's specific needs via text or voice. The intention analysis process is a step of analysing the user's intention of inquiry prior to the answer generation, and has a great influence on the performance of the entire Goal-Oriented Dialogue system. The proposed model was used for a daily chemical products domain and Korean text data related to the domain was used. The analysis is divided into a speech-act which means independent on a specific field concept-sequence and which means depend on a specific field. We propose a classification method using the word embedding model and the CNN as a method for analyzing speech-act and concept-sequence. The semantic information of the word is abstracted through the word embedding model, and concept-sequence and speech-act classification are performed through the CNN based on the semantic information of the abstract word.

• 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.

• Journal of the Korea Convergence Society
• /
• v.13 no.1
• /
• pp.13-21
• /
• 2022

Recent deep learning-based research shows excellent performance in most natural language processing (NLP) fields with pre-trained language models. In particular, the auto-encoder-based language model proves its excellent performance and usefulness in various fields of Korean language understanding. However, the decoder-based Korean generative model even suffers from generating simple sentences. Also, there is few detailed research and data for the field of conversation where generative models are most commonly utilized. Therefore, this paper constructs multi-turn dialogue data for a Korean generative model. In addition, we compare and analyze the performance by improving the dialogue ability of the generative model through transfer learning. In addition, we propose a method of supplementing the insufficient dialogue generation ability of the model by extracting recommended response candidates from external knowledge information through a retrival model.