  A fundamental requirement of any task-oriented dialogue system is the ability
to generate object descriptions that refer to objects in the task domain. The
subproblem of content selection for object descriptions in task-oriented
dialogue has been the focus of much previous work and a large number of models
have been proposed. In this paper, we use the annotated COCONUT corpus of
task-oriented design dialogues to develop feature sets based on Dale and
Reiters (1995) incremental model, Brennan and Clarks (1996) conceptual pact
model, and Jordans (2000b) intentional influences model, and use these feature
sets in a machine learning experiment to automatically learn a model of content
selection for object descriptions. Since Dale and Reiters model requires a
representation of discourse structure, the corpus annotations are used to
derive a representation based on Grosz and Sidners (1986) theory of the
intentional structure of discourse, as well as two very simple representations
of discourse structure based purely on recency. We then apply the
rule-induction program RIPPER to train and test the content selection component
of an object description generator on a set of 393 object descriptions from the
corpus. To our knowledge, this is the first reported experiment of a trainable
content selection component for object description generation in dialogue.
Three separate content selection models that are based on the three theoretical
models, all independently achieve accuracies significantly above the majority
class baseline (17%) on unseen test data, with the intentional influences model
(42.4%) performing significantly better than either the incremental model
(30.4%) or the conceptual pact model (28.9%). But the best performing models
combine all the feature sets, achieving accuracies near 60%. Surprisingly, a
simple recency-based representation of discourse structure does as well as one
based on intentional structure. To our knowledge, this is also the first
empirical comparison of a representation of Grosz and Sidners model of
discourse structure with a simpler model for any generation task.

Computation and Language