tailieunhanh - Báo cáo khoa học: "Detecting problematic turns in human-machine interactions: Rule-induction versus memory-based learning approaches"

We address the issue of on-line detection of communication problems in spoken dialogue systems. The usefulness is investigated of the sequence of system question types and the word graphs corresponding to the respective user utterances. By applying both ruleinduction and memory-based learning techniques to data obtained with a Dutch train time-table information system, the current paper demonstrates that the aforementioned features indeed lead to a method for problem detection that performs significantly above baseline. The results are interesting from a dialogue perspective since they employ features that are present in the majority of spoken dialogue systems and can be. | Detecting problematic turns in human-machine interactions Rule-induction versus memory-based learning approaches Antal van den Bosch ILK Comp. Ling. KUB Tilburg The Netherlands antalb@ Emiel Krahmerl t IPO TU e Eindhoven The Netherlands Marc Swerts Ệ CNTS UIA Antwerp Belgium Abstract We address the issue of on-line detection of communication problems in spoken dialogue systems. The usefulness is investigated of the sequence of system question types and the word graphs corresponding to the respective user utterances. By applying both ruleinduction and memory-based learning techniques to data obtained with a Dutch train time-table information system the current paper demonstrates that the aforementioned features indeed lead to a method for problem detection that performs significantly above baseline. The results are interesting from a dialogue perspective since they employ features that are present in the majority of spoken dialogue systems and can be obtained with little or no computational overhead. The results are interesting from a machine learning perspective since they show that the rule-based method performs significantly better than the memory-based method because the former is better capable of representing interactions between features. 1 Introduction Given the state of the art of current language and speech technology communication problems are unavoidable in present-day spoken dialogue systems. The main source of these problems lies in the imperfections of automatic speech recognition but also incorrect interpretations by the natural language understanding module or wrong default assumptions by the dialogue manager are likely to lead to confusion. If a spoken dialogue system had the ability to detect communication problems on-line and with high accuracy it might be able to correct certain errors or it could interact with the user to solve them. For instance in the case of communication problems it would be .