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.