Donghyeon Lee

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 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.

Unsupervised Spoken Language Understanding for a Multi-Domain Dialog System

This paper proposes an unsupervised spoken language understanding (SLU) framework for a multi-domain dialog system. Our unsupervised SLU framework applies a non-parametric Bayesian approach to dialog acts, intents and slot entities, which are the components of a semantic frame. The proposed approach reduces the human effort necessary to obtain a semantically annotated corpus for dialog system development. In this study, we analyze clustering results using various evaluation metrics for four dialog corpora. We also introduce a multi-domain dialog system that uses the unsupervised SLU framework. We argue that our unsupervised approach can help overcome the annotation acquisition bottleneck in developing dialog systems. To verify this claim, we report a dialog system evaluation, in which our method achieves competitive results in comparison with a system that uses a manually annotated corpus. In addition, we conducted several experiments to explore the effect of our approach on reducing development costs. The results show that our approach be helpful for the rapid development of a prototype system and reducing the overall development costs.

Modeling confirmations for example-based dialog management

This paper proposes a method to model confirmations for example-based dialog management. To enable the system to provide a confirmation to the user in an appropriate time, we employed a multiple dialog state representation approach for keeping track of user input uncertainty and implemented a confirmation agent which decides when the information gathered from the user contains an error. We developed a car navigation dialog system to evaluate our proposed method. Evaluations with simulated dialogs show our approach is useful for handling misunderstanding errors in example-based dialog management.

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.

Example-based error recovery strategy for spoken dialog system

Error handling has become an important issue in spoken dialog systems. We describe an example-based approach to detect and repair errors in an example-based dialog modeling framework. Our approach to error recovery is focused on the re-phrase strategy with a system and a task guidance to help the novice users to re-phrase well-recognizable and well-understandable input. The dialog system gives possible utterance templates and contents related to the current situation when errors are detected. An empirical evaluation of the car navigation system shows that our approach is effective to the novice users for operating the spoken dialog system.

Seamless error correction interface for voice word processor

In this paper, we propose an error correction interface for a voice word processor. This correction interface includes user intention understanding and automatic error region detection. For accurate correction, we include a confirmation process that includes an error region control command and a re-uttering command. We evaluate the performance of the user intention understanding first, and we evaluate the effectiveness of our interface compare to a general two-step error correction interface.

An Example-Based Approach to Ranking Multiple Dialog States for Flexible Dialog Management

This paper presents a new hybrid dialog management framework that integrates a statistical ranking algorithm into an example-based dialog management approach for chat-like dialogs. The proposed model uses ranking features that consider various aspects of dialogs, including the relative importance of speech acts, dialog history sequences, and the causal relationships among speech acts and slot-filling states. The ranking algorithm enables one to aggregate these feature scores systematically and to generate diverse system responses. Additionally, the model provides detailed feedback by analyzing the causal relationships among speech acts and predicting the users possible intentions associated with a given dialog states. Simulated experimental results demonstrate that our approach is effective for task-oriented dialogs and chat-like dialogs. Additionally, a case study using elementary school students implies that the proposed system can be used for language learning purposes in addition to task-oriented services.

A Two-Step Approach for Efficient Domain Selection in Multi-Domain Dialog Systems

This paper discusses a domain selection method for multi-domain dialog systems to generate the most appropriate system utterance in response to a user utterance. We present a two-step approach for efficient domain selection. In our proposed approach, the domain candidates are listed in descending order of scores and then each domain is verified by content-based filtering. When we applied our method, the accuracy increased and the time cost decreased compared to baseline methods.

Unsupervised modeling of user actions in a dialog corpus

In data-driven spoken dialog system development, developers should prepare a dialog corpus with semantic annotation. However, the labeling process is a laborious and time consuming task. To reduce human efforts, we propose an unsupervised approach based on non-parametric Bayesian Hidden Markov Model to the problem of modeling user actions. With the non-parametric model, system designers do not need to determine the number and type of user actions. In the experiments, we evaluated the clustering results by comparing them to the human annotation. We also tested a dialog system that used models trained from the automatically annotated corpus with a user simulation.