Hyungjong Noh

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.

Example-based chat-oriented dialogue system with personalized long-term memory

This study introduces an example-based chat-oriented dialogue system with personalization framework using long-term memory. Previous representative chat-bots use simple keyword and pattern matching methodologies. To maintain the quality of systems, generating numerous heuristic rules with human labour is inevitable. The language expert knowledge is also necessary to build those rules and matching patterns. To avoid high annotation cost, example-based dialogue management is adopted for building chat-oriented dialogue system. We also propose three features: POS-tagged tokens for sentence matching, using NE types and values for searching proper responses, and using back-off responses for unmatched user utterances. Also, our system automatically collects user-related facts from user input sentences and stores the facts into a long-term memory. System responses can be modified by applying user-related facts in the long-term memory. A relevance score of a system response is proposed to select responses that include user-related fact, or frequently used responses. In several experiments, we have found that our proposed features contribute to improve the performance and our system shows competitive performance to ALICE system with the same training corpus.

Ontology-based inference for information-seeking in natural language dialog system

Many natural language dialog systems have been developed with relational database (RDB) as a machine-readable knowledge source. However, RDB has some problems for answering the questions which need complex domain-specific information. In addition to the typical problems of RDB such as dependency and redundancy problems, limitations of meaning representation and storing various domain knowledge problems also exist. To solve the problems of RDB, we adopted ontology concepts as a knowledge representation method. Ontology knowledge has some advantages about representing and querying information. In this paper, we developed ontology-based approach for information-seeking to improve traditional RDB-based natural language dialog systems. This is more flexible than RDB-based dialog system for answering to complex questions. To implement our system, we designed hand-crafted ontology schemas for an electronic program guide (EPG) application and we populated ontology instances from Web pages semi-automatically. We believe that our preliminary evaluations show the possibility of our new system.

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.

Exploiting out-of-vocabulary words for out-of-domain detection in dialog systems

Multi-domain dialog systems often encounter user requests for out-of-domain (OOD) service. This paper focuses on detecting these requests. The proposed OOD detection method is included in a multi-domain detection component naturally. This component consists of multiple in-domain verifiers: an in-domain verifier accepts a user utterance when it belongs to the domain and rejects the utterance otherwise. To detect OOD requests without using an actual OOD corpus, the in-domain verifiers exploit the occurrence of out-of-vocabulary words. In experiments, the proposed OOD detection method was more accurate than three baseline methods.

Engine-Independent ASR Error Management for Dialog Systems

This paper describes a method of ASR (automatic speech recognition) engine independent error correction for a dialog system. The proposed method can correct ASR errors only with a text corpus which is used for training of the target dialog system, and it means that the method is independent of the ASR engine. We evaluated our method on two test corpora (Korean and English) that are parallel corpora including ASR results and their correct transcriptions. Overall results indicate that the method decreases the word error rate of the ASR results and recovers the errors in the important attributes of the dialog system. The method is general and can also be applied to the other speech based applications such as voice question-answering and speech information extraction systems.

Ranking Dialog Acts using Discourse Coherence Indicator for Language Tutoring Dialog Systems

As the importance of learning foreign languages increases, the demand for language learning materials is increasing. A dialog system can be one of useful multimedia resources for language learning. Contrary to information-seeking dialog systems, ranking user/system dialog acts can be very useful for language tutoring purpose. Therefore we propose a novel and general method to rank dialog acts that looks up dialog histories using an enhanced Levenshtein distance based on the impact of each dialog act on the dialog history. Experimental results showed that the proposed model outperformed the baseline system in ranking performance. Also the proposed method exhibited a better task completion rate than the baseline system, implicating the use of the proposed method as a new model for general dialog management. Furthermore, the proposed method could generate more diverse system responses than the baseline system, which is very desirable for the language tutoring purpose.

A Joint Statistical Model for Simultaneous Word Spacing and Spelling Error Correction for Korean

This paper presents noisy-channel based Korean preprocessor system, which corrects word spacing and typographical errors. The proposed algorithm corrects both errors simultaneously. Using Eojeol transition pattern dictionary and statistical data such as Eumjeol n-gram and Jaso transition probabilities, the algorithm minimizes the usage of huge word dictionaries.