Kyusong Lee

On the effectiveness of robot-assisted language learning

This study introduces the educational assistant robots that we developed for foreign language learning and explores the effectiveness of robot-assisted language learning (RALL) which is in its early stages. To achieve this purpose, a course was designed in which students have meaningful interactions with intelligent robots in an immersive environment. A total of 24 elementary students, ranging in age from ten to twelve, were enrolled in English lessons. A pre-test/post-test design was used to investigate the cognitive effects of the RALL approach on the students??? oral skills. No significant difference in the listening skill was found, but the speaking skills improved with a large effect size at the significance level of 0.01. Descriptive statistics and the pre-test/post-test design were used to investigate the affective effects of RALL approach. The result showed that RALL promoted and improved students??? satisfaction, interest, confidence, and motivation at the significance level of 0.01.

DialPort: Connecting the spoken dialog research community to real user data

This paper describes a new spoken dialog portal that connects systems produced by the spoken dialog academic research community and gives them access to real users. We introduce a distributed, multi-modal, multi-agent prototype dialog framework that affords easy integration with various remote resources, ranging from end-to-end dialog systems to external knowledge APIs. The portal provides seamless passage from one spoken dialog system to another. To date, the DialPort portal has successfully connected to the multi-domain spoken dialog system at Cambridge University, the NOAA (National Oceanic and Atmospheric Administration) weather API and the Yelp API. We present statistics derived from log data gathered during preliminary tests of the portal on the performance of the portal and on the quality (seamlessness) of the transition from one system to another.

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.

POSTECH Grammatical Error Correction System in the CoNLL-2014 Shared Task

This paper describes the POSTECH grammatical error correction system. Various methods are proposed to correct errors such as rule-based, probability n-gram vector approaches and router-based approach. Google N-gram count corpus is used mainly as the correction resource. Correction candidates are extracted from NUCLE training data and each candidate is evaluated with development data to extract high precision rules and n-gram frames. Out of 13 participating teams, our system is ranked 4 th on both the original and revised annotation.

DialPort: A General Framework for Aggregating Dialog Systems

This paper describes a new spoken dialog portal that connects systems produced by the spoken dialog research community and gives them access to real users. We introduce a prototype dialog framework that affords easy integration with various remote dialog agents as well as external knowledge resources. To date, the DialPort portal has successfully connected to two dialog systems and several public knowledge APIs. We present current progress and envision our future plan.

Conversational Knowledge Teaching Agent that uses a Knowledge Base

When implementing a conversational educational teaching agent, user-intent understanding and dialog management in a dialog system are not sufficient to give users educational information. In this paper, we propose a conversational educational teaching agent that gives users some educational information or triggers interests on educational contents. The proposed system not only converses with a user but also answer questions that the user asked or asks some educational questions by integrating a dialog system with a knowledge base. We used the Wikipedia corpus to learn the weights between two entities and embedding of properties to calculate similarities for the selection of system questions and answers.

Scalable Summary-State POMDP Hybrid Dialog System for Multiple Goal Drifting Requests and Massive Slot Entity Instances

One of the main problems with partially observable Markov decision process (POMDP) in development of spoken dialog system (SDS) is lack of scalability. In development of an SDS with electronic program guide (EPG) domain, we devised a POMDP approach which is operated with summary spaces to respond accurately to multiple drifting goals and massive numbers of slot entities. The main point of the proposed approach is to introduce a hybrid architecture that is implemented by a meta-action selector and a service provider. A trained POMDP policy was used to select meta-actions. The selected meta-actions were transformed to the system action in the service provider, which is implemented with the given system action model. By using this architecture, various system actions could be elicited with reduced complexity in the dialog process. We trained the system with the specified simulator and observed its behavior with learning curves in the Korean EPG domain. The convergence of learning curve implies the feasibility of our approach in commercial EPG domain SDS.

An Assessment Framework for DialPort

Collecting a large amount of real human-computer interaction data in various domains is a cornerstone in the development of better data-driven spoken dialog systems. The DialPort project is creating a portal to collect a constant stream of real user conversational data on a variety of topics. In order to keep real users attracted to DialPort, it is crucial to develop a robust evaluation framework to monitor and maintain high performance. Different from earlier spoken dialog systems, DialPort has a heterogeneous set of spoken dialog systems gathered under one outward-looking agent. In order to access this new structure, we have identified some unique challenges that DialPort will encounter so that it can appeal to real users and have created a novel evaluation scheme that quantitatively assesses their performance in these situations. We look at assessment from the point of view of the system developer as well as that of the end user.

Open-domain personalized dialog system using user-interested topics in system responses

We built a personalized example-based dialog system that constructs its responses by considering entities that the user has uttered, and topics in which the user has expressed interest. The system analyzes user input utterances, then uses DBpedia and Freebase to extract relevant entities and topics. The extracted entities and topics are stored in personal knowledge memory and are used when the system selects responses from the example database and generates responses. We conducted a human experiment in which evaluators rated dialog systems based on subjective metrics. The proposed dialog system that uses topics that are of interest to the user achieved higher evaluation scores for both personalization and satisfaction than the baseline systems. These results demonstrate that the use of topics in the system response provides a sense that the system pays attention to the users utterances; as a consequence the user has a satisfactory dialog experience.

Sentence completion task using web-scale data

We propose a method to automatically answer SAT-style sentence completion questions using web-scale data. Web-scale da-ta have been used in many language studies and have been found to be a very useful resource for improving accuracy in sentence completion task. Our method employs assorted N-gram probability information for each candidate word. We also proposed back-off strategy was used to remove zero probabilities. We found that the accuracy of our proposed method improved by 52-87% over the current state-of-the-art.