Understanding and Presentation of Conceptual Information based on Planning
-- A Case-Based Approach to Text Planning --
Kuniaki UEHARA, Minoru TANIGUCHI, and Hiroshi OGINO
Department of Computer and Systems Engineering, Kobe University
Nada, Kobe 657, Japan
Almost all current dialogue systems are generalization-based, in the
sense that they use explicit abstract generalizations. Within
computational linguistics, two main approaches have dominated, the
first using schemata to constrain the overall organization of the
text, and the second using plan operators to generate a sequence of
utterances given particular communicative goals. Schema-based
approaches cannot participate in an interactive dialogue with users.
In particular, they cannot elaborate on previous utterances or respond
to follow-up questions in the context of the on-going dialogue.
Plan-based approaches are limited because they know nothing of
stereotypes, and they treat each dialogue as completely novel. This
limitation dooms them to perform expensive text planning every time
they recognize a new instance of dialogue.
In this research, we will propose a case-based text planning approach to dialogue systems in which feedback from the user is an integral part of the text planning process. Case-based planning is the paradigm that devises new plans by retrieving and adapting old ones from memory. The basic model of case-based planning is obviously incomplete if we adopt the model as the text planner of a dialogue system. It is incomplete because this mechanism ignores the following problems: (1) Users frequently ask follow-up questions requesting clarification, elaboration, or re-explanation. (2) Users may misunderstand the explanation if they do not have enough or accurate knowledge about the domain.
In order to deal with the former problem, we make use of a reactive approach which employs feedback from the user to guide subsequent dialogues. To accomplish this task, the system is designed not only to generate and execute text plans, but also to interrupt and modify them, when the user asks a follow-up question or indicates that he does not understand the explanation. In order to deal with the latter problem, we adopt an approach where the system learns to anticipate and avoid user's misunderstanding that it has previously encountered. When the misunderstanding re-occur in later situations, the text planner is reminding of the past misunderstanding and this reminding serves as a warning to the text planner that it has to plan for the fact that this misunderstanding is going to occur again. The text planner also stores the repaired plans that were built in response to past misunderstanding.
The system described here were implemented as a natural language consultation system for the UNIX operating system, called ASSIST-R. Users can ask ASSIST-R how to do things in UNIX, get definitions of UNIX terminology, and get help debugging problems in using commands. Currently, we are using an adaptation of Dyer's McDYPAR for the Japanese query analyzer and Japanese Tree Adjoining Grammar for text generation mechanism.
Keywords: Dialogue system, case-based planning, text planning