Seokhwan Kim

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.

The fifth dialog state tracking challenge

Dialog state tracking - the process of updating the dialog state after each interaction with the user - is a key component of most dialog systems. Following a similar scheme to the fourth dialog state tracking challenge, this edition again focused on human-human dialogs, but introduced the task of cross-lingual adaptation of trackers. The challenge received a total of 32 entries from 9 research groups. In addition, several pilot track evaluations were also proposed receiving a total of 16 entries from 4 groups. In both cases, the results show that most of the groups were able to outperform the provided baselines for each task.

MMR-based Active Machine Learning for Bio Named Entity Recognition

This paper presents a new active learning paradigm which considers not only the uncertainty of the classifier but also the diversity of the corpus. The two measures for uncertainty and diversity were combined using the MMR (Maximal Marginal Relevance) method to give the sampling scores in our active learning strategy. We incorporated MMR-based active machine-learning idea into the biomedical named-entity recognition system. Our experimental results indicated that our strategies for active-learning based sample selection could significantly reduce the human effort.

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.

Sequential Labeling for Tracking Dynamic Dialog States

This paper presents a sequential labeling approach for tracking the dialog states for the cases of goal changes in a dialog session. The tracking models are trained using linear-chain conditional random fields with the features obtained from the results of SLU. The experimental results show that our proposed approach can improve the performances of the sub-tasks of the second dialog state tracking challenge.

R-cube: A dialogue agent for restaurant recommendation and reservation

This paper describes a hybrid dialogue system for restaurant recommendation and reservation. The proposed system combines rule-based and data-driven components by using a flexible architecture aiming at diminishing error propagation along the different steps of the dialogue management and processing pipeline. The system implements three basic subsystems for restaurant recommendation, selection and booking, which leverage on the same system architecture and processing components. The specific system described here operates with a data collection of Singapores F&B industry but it can be easily adapted to any other city or location by simply replacing the used data collection.

SARA: Singapore’s Automated Responsive Assistant, A Multimodal Dialogue System for Touristic Information

In this paper we describe SARA, a multimodal dialogue system offering touristic assistance for visitors coming to Singapore. The system is implemented as an Android mobile phone application and provides information about local attractions, restaurants, sightseeing, direction and transportation services. SARA is able to detect the user’s location on a map by using a GPS integrated module and accordingly can provide real-time orientation and direction help. To communicate with SARA users can use speech, text or scanned QR code. Input/output modalities for SARA include natural language in form of speech or text. A short video about the main features of our Android application can be seen at: http://vimeo.com/91620644. Currently, the system supports only English, but we are working towards a multi-lingual input/output support. For test purposes we also created a web version of SARA that can be tested for Chinese and English text input/output at: http://iris.i2r.a-star.edu.sg/StatTour/.

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.

A Local Tree Alignment-based Soft Pattern Matching Approach for Information Extraction

This paper presents a new soft pattern matching method which aims to improve the recall with minimized precision loss in information extraction tasks. Our approach is based on a local tree alignment algorithm, and an effective strategy for controlling flexibility of the pattern matching will be presented. The experimental results show that the method can significantly improve the information extraction performance.

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.