A Flexible Approach to Cooperative Response Generation in Information-Seeking Dialogues

A FLEXIBLE APPROACH TO COOPERATIVE RESPONSE GENERATION IN INFORMATION-SEEKING DIALOGUES Liliana Ardissono, Alessandro Lombardo, Dario Sestero Dipartimento di Informatica - Universita' di Torino C.so Svizzera 185 - 10149 - Torino - Italy E-Mail: liliana@di.unito.it Abstract This paper presents a cooperative consultation system on a restricted domain. The system builds hypotheses on the user's plan and avoids misunderstandings (with consequent repair dialogues) through clarification dialogues in case of ambiguity. The role played by constraints in the generation of the answer is charac- terized in order to limit the cases of ambiguities re- quiring a clarification dialogue. The answers of the system are generated at different levels of detail, ac- cording to the user's competence in the domain. INTRODUCTION This paper presents a plan-based consultation system for getting information on how to achieve a goal in a restricted domain, l The main purpose of the system is to recognize the user's plans and goals to build coop- erative answers in a flexible way [Allen, 83], [Carberry, 90]. The system is composed of two parts: hypotheses construction and response generation. The construction of hypotheses is based on Context Models (CMs) [Carberry, 90]. Carberry uses default inferences [Carberry, 90b] to select a single hypothe- sis for building the final answer of the system and, in case the choice is incorrect, a repair dialogue is started. Instead, in our system, we consider all plau- sible hypotheses and if the ambiguity among them is relevant for the generation of the response, we try to solve it by starting a clarification dialogue. According to [van Beek and Cohen, 91], clarification dialogues are simpler for the user than repair ones, because they only involve yeshlo questions on the selected ambigu- ous plans. Furthermore, repair dialogues generally re- quire a stronger participation of the user. Finally, if the misunderstanding is not discovered, the system delivers information that is not proper to the user's case. For these reasons, it is preferable to solve the runbiguity a priori, by asking the user information on his intentions. In van Beek and Cohen's approach cl,'wification dialogues are started, even in case the answers associated with the plausible hypotheses are distinguished by features that could dhectly be managed in the answer. We avoid this by identifying the constraints relevant for a clarification dialogue and those which can be mentioned in the answer. In this way, the friendliness of the system is improved lThe system is concerned with information about a CS Deparunent. and the number and the length of the clarification dia- logues are reduced. In the perspective of generating flexible cooperative answers, it is important to differentiate their detail level by adapting them to the user's competence in the domain. In our work, we want to study how to embed information obtained from a user model component in the system. As a first step in this direction, we introduce a preliminary classification of users in three standard levels of competence corresponding to the major users' prototypes the system is devoted to. Then, in …

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