• MALSORI
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• no.56
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• pp.185-194
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• 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
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• 2005.11a
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• pp.113-115
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• 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.

• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.1-10
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• 2004

Dialogue speech corpora that contain sufficient dialogue speech features are needed for performance assessment of a spoken language dialogue system. And labeling information of dialogue speech corpora plays an important role for improvement of recognition rate in acoustic and language models. In this paper, we examine the methods by which labeling information of dialogue speech corpora can be structured. More specifically, we examined how to represent features of dialogue speech in a structured document based XML and how to design the repository system of the information.

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

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.331-332
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• 2022

현대사회에서는 다양한 방법, 통로로 자신들의 의견을 표현하고 또한 감정들을 표출한다. 이렇게 표출된 다양한 문장 및 감정들을 통해 각 연령별로 어떤 문제를 가지고 있는지, 무슨 상황에 놓여있는지 등을 알 수 있다. 본 논문에서는 이렇게 모여진 감성대화 말뭉치를 이용해 청소년들이 문장에서 추출한 단어들과 감정, 상황과 어떠한 연관성을 보이는지 확인해보고자 연구를 진행하였다. 청소년들이 남성의 경우 학교폭력 및 따돌림과 관련한 문제, 여성의 경우 가족관계와 관련한 문제와 연관성이 크다는 것을 확인하였다.

• Proceedings of the KSPS conference
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• 2006.11a
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• pp.14-18
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• 2006

The role of dialogue manager is to select proper actions based on observed environment and inferred user intention. This paper presents stochastic model for dialogue manager based on Markov decision process. To build a mixed initiative dialogue manager, we used accumulated user utterance, previous act of dialogue manager, and domain dependent knowledge as the input to the MDP. We also used dialogue corpus to train the automatically optimized policy of MDP with reinforcement learning algorithm. The states which have unique and intuitive actions were removed from the design of MDP by using the domain knowledge. The design of dialogue manager included the usage of natural language understanding and response generator to build short message based remote control of home networked appliances.

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

• MALSORI
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• no.63
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• pp.101-112
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• 2007

Spoken dialog system development includes many laborious and inefficient tasks. Since there are many components such as speech recognition, language understanding, dialog management and knowledge management in a spoken dialog system, a developer should take an effort to edit corpus and train each model separately. To reduce a cost for editing corpus and training each model, we need more systematic and efficient working environment. For the working environment, we propose DialogStudio as a spoken dialog system workbench.

• Journal of Information Processing Systems
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• v.9 no.2
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• pp.259-270
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• 2013

A speaker's intentions can be represented by domain actions (domain-independent speech act and domain-dependent concept sequence pairs). Therefore, it is essential that domain actions be determined when implementing dialogue systems because a dialogue system should determine users' intentions from their utterances and should create counterpart intentions to the users' intentions. In this paper, a neural network model is proposed for classifying a user's domain actions and planning a system's domain actions. An integrated neural network model is proposed for simultaneously determining user and system domain actions using the same framework. The proposed model performed better than previous non-integrated models in an experiment using a goal-oriented dialogue corpus. This result shows that the proposed integration method contributes to improving domain action determination performance.

• Asia Pacific Journal of Corpus Research
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• v.2 no.1
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• pp.23-34
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• 2021

This research examines Japanese non-native speakers' (JNNS) modal auxiliary verb use from two different perspectives: frequency of use and preferences for modalities. Additionally, error analysis is carried out to identify errors in modal use common among JNNSs. Their modal use is compared to that of English native speakers within a spoken dialogue corpus which is part of the International Corpus Network of Asian Learners' English. Research findings show at a statistically significant level that when compared to native speakers, JNNSs underuse past forms of modals and infrequently convey epistemic modality, indicating the possibility that JNNSs fail to express their opinions or thoughts indirectly when needed or to convey politeness appropriately. Error analysis identifies the following three types of common errors: (1) the use of incorrect tenses of modal verb phrases, (2) the use of inflected verb forms after modals, and (3) the non-use of main verbs after modals. The first type of error is largely because JNNSs do not master how to express past meanings of modals. The second and third types of errors seem to be due to first language transfer into second language acquisition and JNNSs' overgeneralization of the subject-verb agreement rules to modals respectively.