Learning attribute selections for non-pronominal expressions

Abstract

A fundamental function of any task-oriented dialogue system is the ability to generate nominal expressions that describe objects in the task domain. In this paper, we report results from using machine learning to train and test a nominal-expression generator on a set of 393 nominal descriptions from the CO-CONUT corpus of task-oriented design dialogues. Results show that we can achieve a 50% match to human performance as opposed to a 16% baseline for just guessing the most frequent type of nominal expression in the COCONUT corpus. To our surprise our results indicate that many of the central features of previously proposed selection models did not improve the performance of the learned nominal-expression generator.