• MALSORI
• /
• no.56
• /
• pp.185-194
• /
• 2005

In this paper, we present POSSDM (POSTECH Situation-Based Dialogue Manager) for a spoken dialogue system using a new example and situation-based dialogue management technique for effective generation of appropriate system responses. Spoken dialogue system should generate cooperative responses to smoothly control dialogue flow with the users. We introduce a new dialogue management technique incorporating dialogue examples and situation-based rules for EPG (Electronic Program Guide) domain. For the system response inference, we automatically construct and index a dialogue example database from dialogue corpus, and the best dialogue example is retrieved for a proper system response with the query from a dialogue situation including a current user utterance, dialogue act, and discourse history. When dialogue corpus is not enough to cover the domain, we also apply manually constructed situation-based rules mainly for meta-level dialogue management.

• Proceedings of the KSPS conference
• /
• 2005.11a
• /
• pp.113-115
• /
• 2005

In this paper, we present POSSDM (POSTECH Situation-Based Dialogue Manager) for a spoken dialogue system using a new example and situation-based dialogue management techniques for effective generation of appropriate system responses. Spoken dialogue system should generate cooperative responses to smoothly control dialogue flow with the users. We introduce a new dialogue management technique incorporating dialogue examples and situation-based rules for EPG (Electronic Program Guide) domain. For the system response inference, we automatically construct and index a dialogue example database from dialogue corpus, and the best dialogue example is retrieved for a proper system response with the query from a dialogue situation including a current user utterance, dialogue act, and discourse history. When dialogue corpus is not enough to cover the domain, we also apply manually constructed situation-based rules mainly for meta-level dialogue management.

• 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 KIISE:Software and Applications
• /
• v.35 no.1
• /
• pp.33-40
• /
• 2008

In this paper, we propose a method to enhance adaptability in a dialogue system using the reinforcement learning that reduces response errors by trials and error-search similar to a human dialogue process. The adaptive dialogue strategy means that the dialogue system improves users' satisfaction and dialogue efficiency by loaming users' dialogue styles. To apply the reinforcement learning to the dialogue system, we use a main-dialogue span and sub-dialogue spans as the mathematic application units, and evaluate system usability by using features; success or failure, completion time, and error rate in sub-dialogue and the satisfaction in main-dialogue. In addition, we classify users' groups into beginners and experts to increase users' convenience in training steps. Then, we apply reinforcement learning policies according to users' groups. In the experiments, we evaluated the performance of the proposed method on the individual reinforcement learning policy and group's reinforcement learning policy.

• Electronics and Telecommunications Trends
• /
• v.34 no.4
• /
• pp.55-64
• /
• 2019

In this study, we introduce trends in neural-network-based deep learning research applied to dialogue systems. Recently, end-to-end trainable goal-oriented dialogue systems using long short-term memory, sequence-to-sequence models, among others, have been studied to overcome the difficulties of domain adaptation and error recognition and recovery in traditional pipeline goal-oriented dialogue systems. In addition, some research has been conducted on applying reinforcement learning to end-to-end trainable goal-oriented dialogue systems to learn dialogue strategies that do not appear in training corpora. Recent neural network models for end-to-end trainable chit-chat systems have been improved using dialogue context as well as personal and topic information to produce a more natural human conversation. Unlike previous studies that have applied different approaches to goal-oriented dialogue systems and chit-chat systems respectively, recent studies have attempted to apply end-to-end trainable approaches based on deep neural networks in common to them. Acquiring dialogue corpora for training is now necessary. Therefore, future research will focus on easily and cheaply acquiring dialogue corpora and training with small annotated dialogue corpora and/or large raw dialogues.

• The KIPS Transactions:PartB
• /
• v.9B no.6
• /
• pp.817-826
• /
• 2002

In Korean dialogue honorification phenomenon may occur, an honorific pronoun may be used, and a subject or an object may be completely omitted when it can be recovered based on context. This paper proposes that in order to process Korean dialogue in which such distinct linguistic phenomena occur and to construct its representation structure we mark and use the following information explicitly, not implicitly : information about dialogue participants, information about the speech act of an utterance, information about the relative order of social status for the people involved in dialogue, and information flow among utterances of dialogue. In addition, this paper presents a method of marking and using such situational information and an appropriate representation structure of Korean dialogue. In this paper we set up Korean dialogue representation structure by modifying and extending DRT (Discourse Representation Theory) and SDRT (Segmented Discourse Representation Theory). Futhermore, this paper shows how to process Korean dialogue computationally and construct its representation structure by using Prolog programming language, and then applies such representation structure to spontaneous Korean dialogue to know its validity.

• 한국HCI학회:학술대회논문집
• /
• 2009.02a
• /
• pp.257-262
• /
• 2009

This paper studies an automatic expansion of dialogue examples using the logs of an example-based dialogue system. Conventional approaches to example-based dialogue system manually construct dialogue examples between humans and a Chatbot, which are labor intensive and time consuming. The proposed method automatically classifies natural utterance pairs and adds them into dialogue example database. Experimental results show that lexical, POS and modality features are useful for classifying natural utterance pairs, and prove that the dialogue examples can be automatically expanded using the logs of a dialogue system.

• English Language & Literature Teaching
• /
• v.16 no.3
• /
• pp.291-315
• /
• 2010

The present study examined the role of dialogue journaling in Korean students' English writing development. Thirteen Korean middle school students participated in a five-week dialogue journal writing program. The participants' English writing skills before and after their dialogue journaling were compared in terms of holistic and analytical scores of their English essays. The study also examined the changes in the participants' language use, as manifested in their dialogue journals. The results indicated the positive influence of dialogue journal writing on the participants' English writing skills in the areas of content and lexical fluency. As for the linguistic evidence that related dialogue journaling to English writing development, the three patterns of change in language use emerged from the participants' dialogue journals: a) raised awareness on grammar conventions, b) raised awareness on discourse conventions, and c) increased depth and richness in idea development. There were also three unhelpful factors associated with little improvement of English writing: a) lack in basic writing skills, b) repetitive use of identical sentence patterns, and c) lack in grammatical and lexical awareness. Overall, the results suggested that dialogue journaling could facilitate Korean students' English writing development when it creates authentic communicative interactions between the teacher and students.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.11a
• /
• pp.308-310
• /
• 2022

With the rise of the Al wave, task-oriented dialogue systems have become one of the popular research directions in academia and industry. Currently, task-oriented dialogue systems mainly adopt pipelined form, which mainly includes natural language understanding, dialogue state decision making, dialogue state tracking and natural language generation. However, pipelining is prone to error propagation, so many task-oriented dialogue systems in the market are only for single-round dialogues. Usually single- domain dialogues have relatively accurate semantic understanding, while they tend to perform poorly on multi-domain, multi-round dialogue datasets. To solve these issues, we developed a paddle-based multi-domain task-oriented Chinese dialogue system. It is based on NEZHA-base pre-training model and CrossWOZ dataset, and uses intention recognition module, dichotomous slot recognition module and NER recognition module to do DST and generate replies based on rules. Experiments show that the dialogue system not only makes good use of the context, but also effectively addresses long-term dependencies. In our approach, the DST of dialogue tracking state is improved, and our DST can identify multiple slotted key-value pairs involved in the discourse, which eliminates the need for manual tagging and thus greatly saves manpower.

• MALSORI
• /
• no.43
• /
• pp.11-23
• /
• 2002

In this paper, speech style is divided into two - read speech and dialogue. In the experiment, read speech and dialogue use the same sentence to control discrepancy from different sentence. While the number of AP in read speech is less than in dialogue, the number of IP in read speech is more than in dialogue. The number of syllables which consist of AP is more various in dialogue. Intonational patterns of the first AP in IP make a difference. In dialogue, there is a pattern which has many high tones - LHH. The FO range in dialogue is wider than in read speech.