Interpreting and Generating Indirect Answers

This paper presents an implemented computational model for interpreting and generating indirect answers to yes-no questions in English. Interpretation and generation are treated, respectively, as recognition of and construction of a responder's discourse plan for a full answer. An indirect answer is the result of the responder providing only part of the planned response, but intending for his discourse plan to be recognized by the questioner. Discourse plan construction and recognition make use of shared knowledge of discourse strategies, represented in the model by discourse plan operators. In the operators, coherence relations are used to characterize types of information that may accompany each type of answer. Recognizing a mutually plausible coherence relation obtaining between the actual response and a possible direct answer plays an important role in recognizing the responder's discourse plan. During generation, stimulus conditions model a speaker's motivation for selecting a satellite. Also during generation, the speaker uses his own interpretation capability to determine what parts of the plan are inferable by the hearer and thus do not need to be explicitly given. The model provides wider coverage than previous computational models for generating and interpreting indirect answers and extends the plan-based theory of implicature in several ways.