Sangkeun Jung

Example-based dialog modeling for practical multi-domain dialog system

This paper proposes a generic dialog modeling framework for a multi-domain dialog system to simultaneously manage goal-oriented and chat dialogs for both information access and entertainment. We developed a dialog modeling technique using an example-based approach to implement multiple applications such as car navigation, weather information, TV program guidance, and chatbot. Example-based dialog modeling (EBDM) is a simple and effective method for prototyping and deploying of various dialog systems. This paper also introduces the system architecture of multi-domain dialog systems using the EBDM framework and the domain spotting technique. In our experiments, we evaluate our system using both simulated and real users. We expect that our approach can support flexible management of multi-domain dialogs on the same framework.

Data-driven user simulation for automated evaluation of spoken dialog systems

This paper proposes a novel integrated dialog simulation technique for evaluating spoken dialog systems. A data-driven user simulation technique for simulating user intention and utterance is introduced. A novel user intention modeling and generating method is proposed that uses a linear-chain conditional random field, and a two-phase data-driven domain-specific user utterance simulation method and a linguistic knowledge-based ASR channel simulation method are also presented. Evaluation metrics are introduced to measure the quality of user simulation at intention and utterance. Experiments using these techniques were carried out to evaluate the performance and behavior of dialog systems designed for car navigation dialogs and a building guide robot, and it turned out that our approach was easy to set up and showed similar tendencies to real human users.

Recent Approaches to Dialog Management for Spoken Dialog Systems

A field of spoken dialog systems is a rapidly growing research area because the performance improvement of speech technologies motivates the possibility of building systems that a human can easily operate in order to access useful information via spoken languages. Among the components in a spoken dialog system, the dialog management plays major roles such as discourse analysis, database access, error handling, and system action prediction. This survey covers design issues and recent approaches to the dialog management techniques for modeling the dialogs. We also explain the user simulation techniques for automatic evaluation of spoken dialog systems.

Hybrid approach to robust dialog management using agenda and dialog examples

Spoken dialog systems have difficulty selecting which action to take in a given situation because recognition and understanding errors are prevalent due to noise and unexpected inputs. To solve this problem, this paper presents a hybrid approach to improving robustness of the dialog manager by using agenda-based and example-based dialog modeling. This approach can exploit n-best hypotheses to determine the current dialog state in the dialog manager and keep track of the dialog state using a discourse interpretation algorithm based on an agenda graph and a focus stack. Given the agenda graph and multiple recognition hypotheses, the system can predict the next action to maximize multi-level score functions and trigger error recovery strategies to handle exceptional cases due to misunderstandings or unexpected focus shifts. The proposed method was tested by developing a spoken dialog system for a building guidance domain in an intelligent service robot. This system was then evaluated by simulated and real users. The experimental results show that our approach can effectively develop robust dialog management for spoken dialog systems.

A Frame-Based Probabilistic Framework for Spoken Dialog Management Using Dialog Examples

This paper proposes a probabilistic framework for spoken dialog management using dialog examples. To overcome the complexity problems of the classic partially observable Markov decision processes (POMDPs) based dialog manager, we use a frame-based belief state representation that reduces the complexity of belief update. We also used dialog examples to maintain a reasonable number of system actions to reduce the complexity of the optimizing policy. We developed weather information and car navigation dialog system that employed a frame-based probabilistic framework. This framework enables people to develop a spoken dialog system using a probabilistic approach without complexity problem of POMDP.

Using Utterance and Semantic Level Confidence for Interactive Spoken Dialog Clarification

Spoken dialog tasks incur many errors including speech recognition errors, understanding errors, and even dialog management errors. These errors create a big gap between the users intention and the systems understanding, which eventually results in a misinterpretation. To fill in the gap, people in human-to-human dialogs try to clarify the major causes of the misunderstanding to selectively correct them. This paper presents a method of clarification techniques to human-to-machine spoken dialog systems. We viewed the clarification dialog as a two-step problem - Belief confirmation and Clarification strategy establishment. To confirm the belief, we organized the clarification process into three systematic phases. In the belief confirmation phase, we consider the overall dialog systems processes including speech recognition, language understanding and semantic slot and value pairs for clarification dialog management. A clarification expert is developed for establishing clarification dialog strategy. In addition, we proposed a new design of plugging clarification dialog module in a given expert based dialog system. The experiment results demonstrate that the error verifiers effectively catch the word and utterance-level semantic errors and the clarification experts actually increase the dialog success rate and the dialog efficiency.

Hybrid user intention modeling to diversify dialog simulations

This paper proposes a novel user intention simulation method which is data-driven but can integrate diverse user discourse knowledge to simulate various types of user behaviors. A method of data-driven user intention modeling based on logistic regression is introduced in the Markov logic framework. Human dialog knowledge is designed into two layers, domain and discourse knowledge, and integrated with the data-driven model in generation time. Three types of user knowledge, i.e., cooperative, corrective and self-directing, are designed and integrated to generate behaviors of corresponding user-types. In experiments to investigate the patterns of simulated users, the approach successfully generated cooperative, corrective and self-directing user intention patterns.

Correlation-based query relaxation for example-based dialog modeling

Query relaxation refers to the process of reducing the number of constraints on a query if it returns no result when searching a database. This is an important process to enable extraction of an appropriate number of query results because queries that are too strictly constrained may return no result, whereas queries that are too loosely constrained may return too many results. This paper proposes an automated method of correlation-based query relaxation (CBQR) to select an appropriate constraint subset. The example-based dialog modeling framework was used to validate our algorithm. Preliminary results show that the proposed method facilitates the automation of query relaxation. We believe that the CBQR algorithm effectively relaxes constraints on failed queries to return more dialog examples.

DialogStudio: A workbench for data-driven spoken dialog system development and management

Recently, data-driven speech technologies have been widely used to build speech user interfaces. However, developing and managing data-driven spoken dialog systems are laborious and time consuming tasks. Spoken dialog systems have many components and their development and management involves numerous tasks such as preparing the corpus, training, testing and integrating each component for system development and management. In addition, data annotation for natural language understanding and speech recognition is quite burdensome. This paper describes the development of a tool, DialogStudio, to support the development and management of data-driven spoken dialog systems. Desirable aspects of the data-driven spoken dialog system workbench tool are identified, and architectures and concepts are proposed that make DialogStudio efficient in data annotation and system development in a domain and methodology neutral manner. The usability of DialogStudio was validated by developing dialog systems in three different domains with two different dialog management methods. Objective evaluations of each domain show that DialogStudio is a feasible solution as a workbench for data-driven spoken dialog systems.

Hybrid Approach to User Intention Modeling for Dialog Simulation

This paper proposes a novel user intention simulation method which is a data-driven approach but able to integrate diverse user discourse knowledge together to simulate various type of users. In Markov logic framework, logistic regression based data-driven user intention modeling is introduced, and human dialog knowledge are designed into two layers such as domain and discourse knowledge, then it is integrated with the data-driven model in generation time. Cooperative, corrective and self-directing discourse knowledge are designed and integrated to mimic such type of users. Experiments were carried out to investigate the patterns of simulated users, and it turned out that our approach was successful to generate user intention patterns which are not only unseen in the training corpus and but also personalized in the designed direction.