Elements of a Computational Model of Cooperative Response Generation

Elements of a Computational Model of Cooperative Response Generation* Brant A. Cheikes and Bonnie L. Webber University of Pennsylvania Department of Computer and Information Science Philadelphia, PA 19104-6389 brant@linc.cis.upenn.edu bonnie@central.cis.upenn.edu March 9, 1989 Abstract If natural language question-answering (NLQA) systems are to be truly effective and useful, they must respond to queries cooperatively, recognizing and accommodating in their replies a questioner's goals, plans, and needs. This paper concerns the design of cooperative response generation (CRG) systems, NLQA systems that are able to produce integrated cooperative responses. We propose two characteristics of a computational model of cooperative response generation. First, we argue that CRG systems should be able to explicitly reason about and choose among the different response options available to them in a given situation. Second, we suggest that some choices of response content motivate others--that through a process called reflection, respondents detect the need to explain, justify, clarify or otherwise augment information they have already decided to convey. 1 Introduction Our success in day-to-day affairs depends to a great extent on the cooperation of those with whom we interact. Studies of man-machine interaction show that we expect the same from the (complex) systems we deal with [14]. Here we consider the case of natural language question-answering (NLQA) systems. To be cooperative, any system must recognize and accommodate the goals, plans, and needs of its users. For NLQA systems, this means that they must take the initiative when responding, rather than answering queries passively. They cannot simply translate input queries into transactions on database or expert systems--they have to apply many more complex reasoning mechanisms to the task of deciding how to respond. It has been suggested that cooperative NLQA systems must be able to provide extended responses [15, 16], combining such elements as: • a direct answer; • information or action that is pertinent to the direct answer; • information or action pertinent to one or more of the questioner's stated or inferred goals. Cooperative behavior in natural language has been studied by many researchers. At the University of Pennsylvania alone, several projects have focused on different aspects of cooperative response production, *This research was partially supported by DARPA grant N0014-85-K0018. 216 including Kaplan's work on responding when presuppositions fail [7], Mays' work both on responding when queries fail intensionally [8] and on determining competent monitor offers [9], McKeown's TEXT system for explaining concepts known to a database system [11], McCoy's system for correcting object-related misconceptions [10], Hirschberg's work on scalar implicatures and their use in avoiding the production of misleading responses [5], and Pollack's plan inference model for recognizing and responding to discrepancies between the system's and the user's beliefs about domain plans and actions [12]. Other explorations of cooperative communication include [1], [2], [4], [6], [13], [15], and [17]. For more complete references, the reader is referred to Cheikes [3]. The results of these studies have been highly informative. Many different kinds of cooperative behavior have seen identified, and computational models of them proposed. What is of …

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